diff --git a/cookbook/use_cases/intelligence/01_Criminal_Network_Analysis.ipynb b/cookbook/use_cases/intelligence/01_Criminal_Network_Analysis.ipynb
index 51d9b9c7..a17dcb46 100644
--- a/cookbook/use_cases/intelligence/01_Criminal_Network_Analysis.ipynb
+++ b/cookbook/use_cases/intelligence/01_Criminal_Network_Analysis.ipynb
@@ -57,9 +57,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Ignoring invalid distribution ~gno (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
+      "WARNING: Ignoring invalid distribution ~lotly (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
+      "WARNING: Ignoring invalid distribution ~ython-socketio (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
+      "WARNING: Ignoring invalid distribution ~gno (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
+      "WARNING: Ignoring invalid distribution ~lotly (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
+      "WARNING: Ignoring invalid distribution ~ython-socketio (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
+      "WARNING: Ignoring invalid distribution ~gno (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
+      "WARNING: Ignoring invalid distribution ~lotly (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
+      "WARNING: Ignoring invalid distribution ~ython-socketio (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
+     ]
+    }
+   ],
    "source": [
     "%pip install -qU semantica networkx matplotlib plotly pandas faiss-cpu beautifulsoup4 groq sentence-transformers scikit-learn\n"
    ]
@@ -77,13 +100,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
     "\n",
-    "os.environ[\"GROQ_API_KEY\"] = os.getenv(\"GROQ_API_KEY\", \"your-key-here\")\n",
+    "os.environ[\"GROQ_API_KEY\"] = os.getenv(\"GROQ_API_KEY\", \"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\")\n",
     "\n",
     "# Configuration constants\n",
     "EMBEDDING_DIMENSION = 384\n",
@@ -105,9 +128,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🧠 Semantica is ingesting: 404 Client Error: Not Found for url: https://www.justice.gov/opa/pressreleases ❌📥 (1.6s) | 🧠 Semantica is ingesting: 403 Client Error: Forbidden for url: https://www.dea.gov/press-releases ❌📥 (0.4s)  [1/12] Feed: 10 documents\n",
+      "🧠 Semantica is ingesting: 404 Client Error: Not Found for url: https://www.justice.gov/opa/pressreleases ❌📥 (1.6s) | 🧠 Semantica is ingesting: 403 Client Error: Forbidden for url: https://www.dea.gov/press-releases ❌📥 (0.4s)  [2/12] Feed: 10 documents\n",
+      "🧠 Semantica is ingesting: 404 Client Error: Not Found for url: https://www.justice.gov/opa/pressreleases ❌📥 (1.6s) | 🧠 Semantica is ingesting: 403 Client Error: Forbidden for url: https://www.dea.gov/press-releases ❌📥 (0.4s)  [3/12] Feed failed: Failed to parse feed: not well-formed (invalid tok\n",
+      "🧠 Semantica is ingesting: 403 Client Error: Forbidden for url: https://www.dea.gov/press-releases ❌📥 (0.4s) | 🧠 Semantica is ingesting: 404 Client Error: Not Found for url: https://feeds.feedburner.com/oreilly/radar ❌📥 (0.7s)  [4/12] Feed failed: Failed to fetch feed: 404 Client Error: Not Found \n",
+      "🧠 Semantica is ingesting: 404 Client Error: Not Found for url: https://feeds.feedburner.com/oreilly/radar ❌📥 (0.7s) | 🧠 Semantica is ingesting: Ingested 10 items |███████████████| 100.0% ✅📥  [5/12] Feed: 10 documents\n",
+      "🧠 Semantica is ingesting: Ingested 10 items |███████████████| 100.0% ✅📥 | 🧠 Semantica is ingesting: Ingested 10 items |███████████████| 100.0% ✅📥  [6/12] Feed: 10 documents\n",
+      "🧠 Semantica is ingesting: Ingested 10 items |███████████████| 100.0% ✅📥 | 🧠 Semantica is ingesting: Ingested 50 items |███████████████| 100.0% ✅📥  [7/12] Feed: 50 documents\n",
+      "🧠 Semantica is ingesting: Ingested 50 items |███████████████| 100.0% ✅📥 | 🧠 Semantica is ingesting: Ingested 10 items |███████████████| 100.0% ✅📥  [8/12] Feed: 10 documents\n",
+      "🧠 Semantica is ingesting: Ingested 10 items |███████████████| 100.0% ✅📥 | 🧠 Semantica is ingesting: 403 Client Error: Forbidden for url: https://www.bleepingcomputer.com/feed/ ❌📥 (0.5s)  [9/12] Feed failed: Failed to fetch feed: 403 Client Error: Forbidden \n",
+      "🧠 Semantica is ingesting: 403 Client Error: Forbidden for url: https://www.bleepingcomputer.com/feed/ ❌📥 (0.5s) | 🧠 Semantica is ingesting: 403 Client Error: Forbidden for url: https://www.securityweek.com/rss ❌📥 (0.4s)  [10/12] Feed failed: Failed to fetch feed: 403 Client Error: Forbidden \n",
+      "🧠 Semantica is ingesting: 403 Client Error: Forbidden for url: https://www.securityweek.com/rss ❌📥 (0.4s) | 🧠 Semantica is ingesting: Ingested 12 items |███████████████| 100.0% ✅📥  [11/12] Feed: 12 documents\n",
+      "🧠 Semantica is ingesting: Ingested 12 items |███████████████| 100.0% ✅📥 | 🧠 Semantica is ingesting: 404 Client Error: Not Found for url: https://www.csoonline.com/index.rss ❌📥 (1.3s)  [12/12] Feed failed: Failed to fetch feed: 404 Client Error: Not Found \n",
+      "🧠 Semantica is ingesting: Ingested 12 items |███████████████| 100.0% ✅📥 | 🧠 Semantica is ingesting: 404 Client Error: Not Found for url: https://www.csoonline.com/index.rss ❌📥 (1.3s)  [1/7] Web: 3133 characters\n",
+      "🧠 Semantica is ingesting: Ingested 12 items |███████████████| 100.0% ✅📥 | 🧠 Semantica is ingesting: 404 Client Error: Not Found for url: https://www.csoonline.com/index.rss ❌📥 (1.3s)  [2/7] Web: 3861 characters\n",
+      "🧠 Semantica is ingesting: Ingested 12 items |███████████████| 100.0% ✅📥 | 🧠 Semantica is ingesting: 404 Client Error: Not Found for url: https://www.csoonline.com/index.rss ❌📥 (1.3s)  [3/7] Web: 8501 characters\n",
+      "🧠 Semantica is ingesting: 404 Client Error: Not Found for url: https://www.csoonline.com/index.rss ❌📥 (1.3s) | 🧠 Semantica is ingesting: Ingested https://www.us-cert.gov/ncas/alerts (200) |███████████████| 100.0% ✅📥  [4/7] Web: 8501 characters\n",
+      "🧠 Semantica is ingesting: Ingested https://www.us-cert.gov/ncas/alerts (200) |███████████████| 100.0% ✅📥 | 🧠 Semantica is ingesting: Ingested https://www.europol.europa.eu/newsroom (200) |███████████████| 100.0% ✅📥  [5/7] Web: 96 characters\n",
+      "🧠 Semantica is ingesting: Ingested https://www.europol.europa.eu/newsroom (200) |███████████████| 100.0% ✅📥 | 🧠 Semantica is ingesting: Ingested https://www.ncsc.gov.uk/news (200) |███████████████| 100.0% ✅📥  [6/7] Web: 50 characters\n",
+      "🧠 Semantica is ingesting: Ingested https://www.ncsc.gov.uk/news (200) |███████████████| 100.0% ✅📥 | 🧠 Semantica is ingesting: HTTPSConnectionPool(host='www.cyber.gov.au', port=443): Max retries exceeded with url: /news (Caused by ReadTimeoutError(\"HTTPSConnectionPool(host='www.cyber.gov.au', port=443): Read timed out. (read timeout=30)\")) ❌📥 (119.3s)  [7/7] Web failed: Failed to fetch URL: HTTPSConnectionPool(host='www\n",
+      "Ingested 118 documents\n"
+     ]
+    }
+   ],
    "source": [
     "from semantica.ingest import FeedIngestor, WebIngestor, FileIngestor\n",
     "from contextlib import redirect_stderr\n",
@@ -121,46 +171,196 @@
     "# Ingest from OSINT RSS feeds\n",
     "osint_feeds = [\n",
     "    \"https://www.us-cert.gov/ncas/alerts.xml\",\n",
-    "    \"https://www.fbi.gov/feeds/news\"\n",
+    "    \"https://www.europol.europa.eu/rss.xml\",\n",
+    "    \"https://www.treasury.gov/resource-center/sanctions/OFAC-Enforcement/Pages/rss.xml\",\n",
+    "    \"https://feeds.feedburner.com/oreilly/radar\",\n",
+    "    \"https://krebsonsecurity.com/feed/\",\n",
+    "    \"https://www.schneier.com/feed/\",\n",
+    "    \"https://www.darkreading.com/rss.xml\",\n",
+    "    \"https://threatpost.com/feed/\",\n",
+    "    \"https://www.bleepingcomputer.com/feed/\",\n",
+    "    \"https://www.securityweek.com/rss\",\n",
+    "    \"https://www.infosecurity-magazine.com/rss/news/\",\n",
+    "    \"https://www.csoonline.com/index.rss\"\n",
     "]\n",
     "\n",
-    "for feed_url in osint_feeds:\n",
+    "feed_ingestor = FeedIngestor()\n",
+    "for i, feed_url in enumerate(osint_feeds, 1):\n",
     "    try:\n",
     "        with redirect_stderr(StringIO()):\n",
-    "            feed_ingestor = FeedIngestor()\n",
-    "            feed_docs = feed_ingestor.ingest(feed_url, method=\"rss\")\n",
-    "            documents.extend(feed_docs)\n",
-    "    except Exception:\n",
+    "            feed_data = feed_ingestor.ingest_feed(feed_url, validate=False)\n",
+    "            \n",
+    "            feed_count = 0\n",
+    "            for item in feed_data.items:\n",
+    "                if not item.content:\n",
+    "                    item.content = item.description or item.title or \"\"\n",
+    "                if item.content:\n",
+    "                    if not hasattr(item, 'metadata'):\n",
+    "                        item.metadata = {}\n",
+    "                    item.metadata['source'] = feed_url\n",
+    "                    documents.append(item)\n",
+    "                    feed_count += 1\n",
+    "            \n",
+    "            if feed_count > 0:\n",
+    "                print(f\"  [{i}/{len(osint_feeds)}] Feed: {feed_count} documents\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"  [{i}/{len(osint_feeds)}] Feed failed: {str(e)[:50]}\")\n",
+    "        pass\n",
+    "\n",
+    "# Web ingestion from various intelligence and security sources\n",
+    "web_links = [\n",
+    "    \"https://www.interpol.int/en/How-we-work/Notices/View-Red-Notices\",\n",
+    "    \"https://www.unodc.org/unodc/en/data-and-analysis/index.html\",\n",
+    "    \"https://www.cisa.gov/news-events/cybersecurity-advisories\",\n",
+    "    \"https://www.us-cert.gov/ncas/alerts\",\n",
+    "    \"https://www.europol.europa.eu/newsroom\",\n",
+    "    \"https://www.ncsc.gov.uk/news\",\n",
+    "    \"https://www.cyber.gov.au/news\"\n",
+    "]\n",
+    "\n",
+    "web_ingestor = WebIngestor(respect_robots=False, delay=1.0)\n",
+    "for i, web_url in enumerate(web_links, 1):\n",
+    "    try:\n",
+    "        with redirect_stderr(StringIO()):\n",
+    "            web_content = web_ingestor.ingest_url(web_url)\n",
+    "            if web_content and web_content.text:\n",
+    "                # Add content attribute for compatibility with parser\n",
+    "                web_content.content = web_content.text\n",
+    "                if not hasattr(web_content, 'metadata'):\n",
+    "                    web_content.metadata = {}\n",
+    "                web_content.metadata['source'] = web_url\n",
+    "                documents.append(web_content)\n",
+    "                print(f\"  [{i}/{len(web_links)}] Web: {len(web_content.text)} characters\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"  [{i}/{len(web_links)}] Web failed: {str(e)[:50]}\")\n",
     "        pass\n",
     "\n",
     "# Example: Web ingestion from FBI API (commented - requires authentication)\n",
     "# web_ingestor = WebIngestor()\n",
-    "# fbi_docs = web_ingestor.ingest(\"https://api.fbi.gov/wanted/v1/list\", method=\"api\")\n",
-    "\n",
-    "# Fallback: Sample criminal network data\n",
-    "if not documents:\n",
-    "    network_data = \"\"\"\n",
-    "    John Smith is associated with criminal organization XYZ.\n",
-    "    Jane Doe has connections to John Smith and organization XYZ.\n",
-    "    Event: Meeting on 2024-01-15 between John Smith and Jane Doe at Location A.\n",
-    "    Organization XYZ is linked to multiple criminal activities.\n",
-    "    Person: Mike Johnson connected to organization XYZ.\n",
-    "    Location A is a known meeting point for criminal activities.\n",
-    "    Relationship: John Smith and Jane Doe have a business relationship.\n",
-    "    \"\"\"\n",
-    "    with open(\"data/criminal_network.txt\", \"w\", encoding=\"utf-8\") as f:\n",
-    "        f.write(network_data)\n",
-    "    file_ingestor = FileIngestor()\n",
-    "    documents = file_ingestor.ingest(\"data/criminal_network.txt\")\n",
+    "# fbi_docs = web_ingestor.ingest_url(\"https://api.fbi.gov/wanted/v1/list\")\n",
     "\n",
     "print(f\"Ingested {len(documents)} documents\")\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parsing 118 documents...\n",
+      "🧠 Semantica is ingesting: HTTPSConnectionPool(host='www.cyber.gov.au', port=443): Max retries exceeded with url: /news (Caused by ReadTimeoutError(\"HTTPSConnectionPool(host='www.cyber.gov.au', port=443): Read timed out. (read timeout=30)\")) ❌📥 (119.3s) | 🧠 Semantica is parsing: Document: p>\n",
+      "🧠 Semantica is parsing: Document: p>                                                                                   \n",
+      " 🔄🔍 (0.0s) | 🧠 Semantica is parsing: Document file not found: <div>\n",
+      "<h2><strong>Summary<\\strong><\\h2>\n",
+      "<\\div>\n",
+      "<p>Unpatched Pulse Secure VPN servers continue to be an attractive target for malicious actors. Affected organizations that have not applied the software patch to fix an arbitrary file reading vulnerability, known as CVE-2019-11510, can become compromised in an attack.[<a href=\"https:\\nvd.nist.gov\\vuln\\detail\\CVE-2019-11510\" target=\"_blank\" title=\"[1]\">1<\\a>]<\\p>\n",
+      "<p>Although Pulse Secure [2] disclosed the vulnerability and provided software patches for the various affected products in April 2019, the Cybersecurity and Infrastructure Security Agency (CISA) continues to observe wide exploitation of CVE-2019-11510.[<a href=\"https:\\www.kb.cert.org\\vuls\\id\\927237\\\" target=\"_blank\" title=\"[3]\">3<\\a>],[<a href=\"https:\\www.cisa.gov\\news-events\\alerts\\2019\\07\\26\\vulnerabilities-multiple-vpn-applications\" title=\"[4]\">4<\\a>],[<a href=\"https:\\www.cisa.gov\\news-events\\alerts\\2019\\10\\16\\multiple-vulnerabilities-pulse-secure-vpn\" title=\"[5]\">5<\\a>]<\\p>\n",
+      "<p>CISA expects to see continued attacks exploiting unpatched Pulse Secure VPN environments and strongly urges users and administrators to upgrade to the corresponding fixes.[2]<\\p>\n",
+      "<h3>Timelines of Specific Events<\\h3>\n",
+      "<ul>\n",
+      "<li>April 24, 2019 – Pulse Secure releases initial advisory and software updates addressing multiple vulnerabilities.<\\li>\n",
+      "<li>May 28, 2019 – Large commercial vendors get reports of vulnerable VPN through HackerOne.<\\li>\n",
+      "<li>July 31, 2019 – Full use of exploit demonstrated using the admin session hash to get complete shell.<\\li>\n",
+      "<li>August 8, 2019 – Meh Chang and Orange Tsai demonstrate the VPN issues across multiple vendors (Pulse Secure) with detailed attack on active VPN exploitation.<\\li>\n",
+      "<li>August 24, 2019 – Bad Packets identifies over 14,500 vulnerable VPN servers globally still unpatched and in need of an upgrade.<\\li>\n",
+      "<li>October 7, 2019 – The National Security Agency (NSA) produces a Cybersecurity Advisory on Pulse Secure and other VPN products being targeted actively by advanced persistent threat actors.<\\li>\n",
+      "<li>October 16, 2019 – The CERT Coordination Center (CERT\\CC) releases Vulnerability Note VU#927237: Pulse Secure VPN contains multiple vulnerabilities.<\\li>\n",
+      "<li>January 2020 – Media reports cybercriminals now targeting unpatched Pulse Secure VPN servers to install REvil (Sodinokibi) ransomware. &nbsp;&nbsp;<\\li>\n",
+      "<\\ul>\n",
+      "<div>\n",
+      "<h2><strong>Technical Details<\\strong><\\h2>\n",
+      "<h3>Impact<\\h3>\n",
+      "<\\div>\n",
+      "<p>A remote, unauthenticated attacker may be able to compromise a vulnerable VPN server. The attacker may be able to gain access to all active users and their plain-text credentials. It may also be possible for the attacker to execute arbitrary commands on each VPN client as it successfully connects to the VPN server.<\\p>\n",
+      "<p>Affected versions:<\\p>\n",
+      "<ul>\n",
+      "<li>Pulse Connect Secure 9.0R1 - 9.0R3.3<\\li>\n",
+      "<li>Pulse Connect Secure 8.3R1 - 8.3R7<\\li>\n",
+      "<li>Pulse Connect Secure 8.2R1 - 8.2R12<\\li>\n",
+      "<li>Pulse Connect Secure 8.1R1 - 8.1R15<\\li>\n",
+      "<li>Pulse Policy Secure 9.0R1 - 9.0R3.1<\\li>\n",
+      "<li>Pulse Policy Secure 5.4R1 - 5.4R7<\\li>\n",
+      "<li>Pulse Policy Secure 5.3R1 - 5.3R12<\\li>\n",
+      "<li>Pulse Policy Secure 5.2R1 - 5.2R12<\\li>\n",
+      "<li>Pulse Policy Secure 5.1R1 - 5.1R15<\\li>\n",
+      "<\\ul>\n",
+      "<div>\n",
+      "<h2><strong>Mitigations<\\strong><\\h2>\n",
+      "<\\div>\n",
+      "<p>This vulnerability has no viable workarounds except for applying the patches provided by the vendor and performing required system updates.<\\p>\n",
+      "<p>CISA strongly urges users and administrators to upgrade to the corresponding fixes.[2]<\\p>\n",
+      "<div>\n",
+      "<h2><strong>References<\\strong><\\h2>\n",
+      "<p>[1] <a href=\"https:\\nvd.nist.gov\\vuln\\detail\\CVE-2019-11510\" target=\"_blank\" title=\"CVE-2019-11510 Detail\">NIST NVD CVE-2019-11510<\\a><br>[2] Pulse Secure Advisory SA44101<br>[3] <a href=\"https:\\www.kb.cert.org\\vuls\\id\\927237\\\" title=\"Pulse Secure VPN contains multiple vulnerabilities Vulnerability Note VU#927237\" target=\"_blank\">CERT\\CC Vulnerability Note VU#927237<\\a><br>[4] <a href=\"https:\\www.cisa.gov\\news-events\\alerts\\2019\\07\\26\\vulnerabilities-multiple-vpn-applications\" title=\"Vulnerabilities in Multiple VPN Applications\">Vulnerabilities in Multiple VPN Applications | CISA<\\a><br>[5] <a href=\"https:\\www.cisa.gov\\news-events\\alerts\\2019\\10\\16\\multiple-vulnerabilities-pulse-secure-vpn\" title=\"Multiple Vulnerabilities in Pulse Secure VPN\">Multiple Vulnerabilities in Pulse Secure VPN | CISA<\\a><\\p>\n",
+      "<h2><strong>Revisions<\\strong><\\h2>\n",
+      "<\\div>\n",
+      "<p><strong>January 10, 2020:<\\strong> Initial Version<br><strong>April 15, 2020:<\\strong> Revised to correct type of vulnerability.<\\p>\n",
+      "🧠 Semantica is parsing: Document file not found: <div>                                                                 \n",
+      "<h2><strong>Summary<\\strong><\\h2>\n",
+      "<\\div>\n",
+      "<p>Unpatched Pulse Secure VPN servers continue to be an attractive target for malicious actors. Affected organizations that have not applied the software patch to fix an arbitrary file reading vulnerability, known as CVE-2019-11510, can become compromised in an attack.[<a href=\"https:\\nvd.nist.gov\\vuln\\detail\\CVE-2019-11510\" target=\"_blank\" title=\"[1]\">1<\\a>]<\\p>\n",
+      "<p>Although Pulse Secure [2] disclosed the vulnerability and provided software patches for the various affected products in April 2019, the Cybersecurity and Infrastructure Security Agency (CISA) continues to observe wide exploitation of CVE-2019-11510.[<a href=\"https:\\www.kb.cert.org\\vuls\\id\\927237\\\" target=\"_blank\" title=\"[3]\">3<\\a>],[<a href=\"https:\\www.cisa.gov\\news-events\\alerts\\2019\\07\\26\\vulnerabilities-multiple-vpn-applications\" title=\"[4]\">4<\\a>],[<a href=\"https:\\www.cisa.gov\\news-events\\alerts\\2019\\10\\16\\multiple-vulnerabilities-pulse-secure-vpn\" title=\"[5]\">5<\\a>]<\\p>\n",
+      "<p>CISA expects to see continued attacks exploiting unpatched Pulse Secure VPN environments and strongly urges users and administrators to upgrade to the corresponding fixes.[2]<\\p>\n",
+      "<h3>Timelines of Specific Events<\\h3>\n",
+      "<ul>\n",
+      "<li>April 24, 2019 – Pulse Secure releases initial advisory and software updates addressing multiple vulnerabilities.<\\li>\n",
+      "<li>May 28, 2019 – Large commercial vendors get reports of vulnerable VPN through HackerOne.<\\li>\n",
+      "<li>July 31, 2019 – Full use of exploit demonstrated using the admin session hash to get complete shell.<\\li>\n",
+      "<li>August 8, 2019 – Meh Chang and Orange Tsai demonstrate the VPN issues across multiple vendors (Pulse Secure) with detailed attack on active VPN exploitation.<\\li>\n",
+      "<li>August 24, 2019 – Bad Packets identifies over 14,500 vulnerable VPN servers globally still unpatched and in need of an upgrade.<\\li>\n",
+      "<li>October 7, 2019 – The National Security Agency (NSA) produces a Cybersecurity Advisory on Pulse Secure and other VPN products being targeted actively by advanced persistent threat actors.<\\li>\n",
+      "<li>October 16, 2019 – The CERT Coordination Center (CERT\\CC) releases Vulnerability Note VU#927237: Pulse Secure VPN contains multiple vulnerabilities.<\\li>\n",
+      "<li>January 2020 – Media reports cybercriminals now targeting unpatched Pulse Secure VPN servers to install REvil (Sodinokibi) ransomware. &nbsp;&nbsp;<\\li>\n",
+      "<\\ul>\n",
+      "<div>\n",
+      "<h2><strong>Technical Details<\\strong><\\h2>\n",
+      "<h3>Impact<\\h3>\n",
+      "<\\div>\n",
+      "<p>A remote, unauthenticated attacker may be able to compromise a vulnerable VPN server. The attacker may be able to gain access to all active users and their plain-text credentials. It may also be possible for the attacker to execute arbitrary commands on each VPN client as it successfully connects to the VPN server.<\\p>\n",
+      "<p>Affected versions:<\\p>\n",
+      "<ul>\n",
+      "<li>Pulse Connect Secure 9.0R1 - 9.0R3.3<\\li>\n",
+      "<li>Pulse Connect Secure 8.3R1 - 8.3R7<\\li>\n",
+      "<li>Pulse Connect Secure 8.2R1 - 8.2R12<\\li>\n",
+      "<li>Pulse Connect Secure 8.1R1 - 8.1R15<\\li>\n",
+      "<li>Pulse Policy Secure 9.0R1 - 9.0R3.1<\\li>\n",
+      "<li>Pulse Policy Secure 5.4R1 - 5.4R7<\\li>\n",
+      "<li>Pulse Policy Secure 5.3R1 - 5.3R12<\\li>\n",
+      "<li>Pulse Policy Secure 5.2R1 - 5.2R12<\\li>\n",
+      "<li>Pulse Policy Secure 5.1R1 - 5.1R15<\\li>\n",
+      "<\\ul>\n",
+      "<div>\n",
+      "<h2><strong>Mitigations<\\strong><\\h2>\n",
+      "<\\div>\n",
+      "<p>This vulnerability has no viable workarounds except for applying the patches provided by the vendor and performing required system updates.<\\p>\n",
+      "<p>CISA strongly urges users and administrators to upgrade to the corresponding fixes.[2]<\\p>\n",
+      "<div>\n",
+      "<h2><strong>References<\\strong><\\h2>\n",
+      "<p>[1] <a href=\"https:\\nvd.nist.gov\\vuln\\detail\\CVE-2019-11510\" target=\"_blank\" title=\"CVE-2019-11510 Detail\">NIST NVD CVE-2019-11510<\\a><br>[2] Pulse Secure Advisory SA44101<br>[3] <a href=\"https:\\www.kb.cert.org\\vuls\\id\\927237\\\" title=\"Pulse Secure VPN contains multiple vulnerabilities Vulnerability Note VU#927237\" target=\"_blank\">CERT\\CC Vulnerability Note VU#927237<\\a><br>[4] <a href=\"https:\\www.cisa.gov\\news-events\\alerts\\2019\\07\\26\\vulnerabilities-multiple-vpn-applications\" title=\"Vulnerabilities in Multiple VPN Applications\">Vulnerabilities in Multiple VPN Applications | CISA<\\a><br>[5] <a href=\"https:\\www.cisa.gov\\news-events\\alerts\\2019\\10\\16\\multiple-vulnerabilities-pulse-secure-vpn\" title=\"Multiple Vulnerabilities in Pulse Secure VPN\">Multiple Vulnerabilities in Pulse Secure VPN | CISA<\\a><\\p>\n",
+      "<h2><strong>Revisions<\\strong><\\h2>\n",
+      "<\\div>\n",
+      "<p><strong>January 10, 2020:<\\strong> Initial Version<br><strong>April 15, 2020:<\\strong> Revised to correct type of vulnerability.<\\p>\n",
+      "🧠 Semantica is parsing: Document: An intermittent outage at Cloudflare on Tuesday briefly knocked many of the Internet's top destinations offline. Some affected Cloudflare customers were able to pivot away from the platform temporarily so that visitors could still access their websites. But security experts say doing so may have also triggered an impromptu network penetration test for organizations that have come to rely on Cloudflare to block many types of abusive and malicious traffic. 🔄🔍 (0.0s) | 🧠 Semantica is parsing: Document file not found: <p>This is <a href=\"https:\\www.koi.ai\\blog\\urban-vpn-browser-extension-ai-conversations-data-collection\">pretty scary<\\a>:<\\p>rily so that visitors could still access their websites. But security experts say doing so may have also triggered an impromptu network penetration test for organizations that have come to rely on Cloudflare to block many types of abusive and malicious traffic. 🔄🔍 (0.0s)\n",
+      "<blockquote><p>Urban VPN Proxy targets conversations across ten AI platforms: ChatGPT, Claude, Gemini, Microsoft Copilot, Perplexity, DeepSeek, Grok (xAI), Meta AI.<\\p>\n",
+      "<p>For each platform, the extension includes a dedicated &#8220;executor&#8221; script designed to intercept and capture conversations. The harvesting is enabled by default through hardcoded flags in the extension&#8217;s configuration.<\\p>\n",
+      "<p>There is no user-facing toggle to disable this. The only way to stop the data collection is to uninstall the extension entirely.<\\p>\n",
+      "<p>[&#8230;]<\\p>\n",
+      "🧠 Semantica is parsing: Document file not found: <p>This is <a href=\"https:\\www.koi.ai\\blog\\urban-vpn-browser-extension-ai-conversations-data-collection\">pretty scary<\\a>:<\\p>te> ❌🔍 (0.0s)\n",
+      "<blockquote><p>Urban VPN Proxy targets conversations across ten AI platforms: ChatGPT, Claude, Gemini, Microsoft Copilot, Perplexity, DeepSeek, Grok (xAI), Meta AI.<\\p>\n",
+      "<p>For each platform, the extension includes a dedicated &#8220;executor&#8221; script designed to intercept and capture conversations. The harvesting is enabled by default through hardcoded flags in the extension&#8217;s configuration.<\\p>\n",
+      "<p>There is no user-facing toggle to disable this. The only way to stop the data collection is to uninstall the extension entirely.<\\p>\n",
+      "<p>[&#8230;]<\\p>\n",
+      "<p>The data collection operates independently of the VPN functionality. Whether the VPN is connected or not, the harvesting runs continuously in the background...<\\p><\\blockquote> ❌🔍 (0.0s) | 🧠 Semantica is parsing: Document file not found: Developers are leaning more heavily on AI for code generation, but in 2026, the development pipeline and security need to be prioritized. ❌🔍 (0.0s)  Parsed 50/118 documents...\n",
+      "🧠 Semantica is parsing: Document file not found: Amazon detailed a long-running campaign by Russia against critical infrastructure organizations, particularly in the energy sector. ❌🔍 (0.0s) | 🧠 Semantica is parsing: Document: Using artificial intelligence in operational technology environments could be a bumpy ride full of trust issues and security challenges. 🔄🔍 (0.0s)in the energy sector. ❌🔍 (0.0s)(0.0s)  Parsed 100/118 documents...\n",
+      "🧠 Semantica is parsing: Document: Nissan has revealed that over 20,000 customers have had personal information compromised in a third-party data breach  🔄🔍 (0.0s) | 🧠 Semantica is parsing: Document file not found: Open-source server monitoring tool, Nezha, is being exploited by attackers for remote system control ❌🔍 (0.0s)ird-party data breach  🔄🔍 (0.0s) trends ❌🔍 (0.0s)  Parsed 118/118 documents...\n",
+      "Parsed 118 documents\n"
+     ]
+    }
+   ],
    "source": [
     "from semantica.parse import DocumentParser\n",
     "from contextlib import redirect_stderr\n",
@@ -199,9 +399,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Normalizing 118 documents...\n",
+      "🧠 Semantica is parsing: Document file not found: Open-source server monitoring tool, Nezha, is being exploited by attackers for remote system control ❌🔍 (0.0s) | 🧠 Semantica is normalizing |███████████████| 100.0% ✅🔧  Normalized 50/118 documents...\n",
+      "🧠 Semantica is parsing: Document file not found: Open-source server monitoring tool, Nezha, is being exploited by attackers for remote system control ❌🔍 (0.0s) | 🧠 Normalizing text 🔄🔧 (0.0s)  Normalized 100/118 documents...\n",
+      "🧠 Semantica is parsing: Document file not found: Open-source server monitoring tool, Nezha, is being exploited by attackers for remote system control ❌🔍 (0.0s) | 🧠 Semantica is normalizing |███████████████| 100.0% ✅🔧  Normalized 118/118 documents...\n",
+      "Chunking 118 documents...\n",
+      "🧠 Semantica is normalizing |███████████████| 100.0% ✅🔧 | 🧠 Semantica is extracting: Extracted 2 entities using ml |███████████████| 100.0% ✅🎯��  Chunked 50/118 documents (97 chunks so far)\n",
+      "🧠 Semantica is normalizing |███████████████| 100.0% ✅🔧 | 🧠 Semantica is extracting: Extracted 3 entities using ml |███████████████| 100.0% ✅🎯  Chunked 100/118 documents (147 chunks so far)\n",
+      "🧠 Semantica is normalizing |███████████████| 100.0% ✅🔧 | 🧠 Semantica is extracting: Extracted 1 entities using ml |███████████████| 100.0% ✅🎯🎯  Chunked 118/118 documents (171 chunks so far)\n",
+      "Created 171 chunks from 118 documents\n"
+     ]
+    }
+   ],
    "source": [
     "from semantica.normalize import TextNormalizer\n",
     "from semantica.split import TextSplitter\n",
@@ -264,37 +480,26 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🧠 Semantica is extracting: Extracted 19 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracted 0 relations |███████████████| 100.0% ✅🎯�████████████| 100.0% ✅🎯Extracted 289 entities\n"
+     ]
+    }
+   ],
    "source": [
     "from semantica.semantic_extract import NERExtractor\n",
-    "from contextlib import redirect_stderr\n",
-    "from io import StringIO\n",
     "\n",
-    "extractor = NERExtractor(\n",
-    "    provider=\"groq\",\n",
-    "    model=\"llama-3.1-8b-instant\"\n",
-    ")\n",
-    "\n",
-    "entity_types = [\n",
-    "    \"Person\", \"Organization\", \"Event\", \"Location\", \"Relationship\"\n",
-    "]\n",
+    "extractor = NERExtractor(method=\"ml\", model=\"en_core_web_sm\")\n",
+    "chunks_to_process = chunked_docs[:10]\n",
+    "entity_results = extractor.extract(chunks_to_process)\n",
     "\n",
     "all_entities = []\n",
-    "chunks_to_process = chunked_docs[:10]  # Limit for demo\n",
-    "print(f\"Extracting entities from {len(chunks_to_process)} chunks...\")\n",
-    "for i, chunk in enumerate(chunks_to_process, 1):\n",
-    "    try:\n",
-    "        with redirect_stderr(StringIO()):\n",
-    "            entities = extractor.extract(\n",
-    "                chunk,\n",
-    "                entity_types=entity_types\n",
-    "            )\n",
-    "            all_entities.extend(entities)\n",
-    "    except Exception:\n",
-    "        pass\n",
-    "    \n",
-    "    if i % 5 == 0 or i == len(chunks_to_process):\n",
-    "        print(f\"  Processed {i}/{len(chunks_to_process)} chunks ({len(all_entities)} entities found)\")\n",
+    "relevant_types = [\"PERSON\", \"ORG\", \"GPE\", \"LOC\", \"EVENT\", \"DATE\"]\n",
+    "for entities in entity_results:\n",
+    "    all_entities.extend([e for e in entities if e.label in relevant_types])\n",
     "\n",
     "print(f\"Extracted {len(all_entities)} entities\")\n"
    ]
@@ -314,84 +519,58 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🧠 Semantica is extracting: Extracted 19 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Starting batch extraction... 0/10 (remaining: 10) |░░░░░░░░░░░░░░░| 0.0% [0/10] 🔄🎯"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🧠 Semantica is extracting: Extracted 19 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracting relations using pattern... 🔄🎯 (1.0s)DEBUG: Entity map keys: [\"chunk(text='<div>\\\\n\", 'australia', 'canada', 'new zealand', 'the united kingdom', 'united states.[1][2][3][4][5]</p>\\\\n', 'li>webshell', 'china', 'li>c2 obfuscation', \"entity(text='canada\", 'confidence=1.0', \"entity(text='new zealand'\", \"new zealand'\", \"entity(text='the united kingdom'\", \"the united kingdom'\", 'end_char=482']\n",
+      "🧠 Semantica is extracting: Extracted 19 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracting relations using pattern... 🔄🎯 (0.7s)DEBUG: Entity map keys: ['today']\n",
+      "🧠 Semantica is extracting: Extracted 19 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracting relations using pattern... 🔄🎯 (1.0s)DEBUG: Entity map keys: ['may 2015', 'frutas', 'wireshark', 'advanced persistent threat', 'apt', 'quasar', 'java', 'mac', 'pii', 'early 2018', 'early 2017', 'society for worldwide interbank financial telecommunication', 'windows registry', 'task', 'li>significant', 'ip', 'end_char=2514', \"entity(text='may 2015'\", \"may 2015'\", 'confidence=1.0', 'end_char=4132', \"entity(text='mac\", 'end_char=4752', \"entity(text='early 2018'\", \"entity(text='early 2017'\", \"worldwide interbank financial telecommunication'\", 'registry']\n",
+      "🧠 Semantica is extracting: Extracted 19 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracting relations using pattern... 🔄🎯 (0.8s)0s)DEBUG: Entity map keys: ['the united kingdom national cyber security centre', 'uk', 'china', '>china chopper', 'china chopper', '2869', \"united kingdom national cyber security centre'\", 'end_char=7491', \"the united kingdom national cyber security centre'\", 'confidence=1.0', 'chopper', \"entity(text='>china chopper'\"]\n",
+      "🧠 Semantica is extracting: Extracted 19 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracting relations using pattern... 🔄🎯 (0.7s)DEBUG: Entity map keys: ['summer 2018', 'cve-2017-3066', 'adobe coldfusion', 'china chopper', '1327', '2018', \"summer 2018'\", \"entity(text='cve-2017-3066'\", 'coldfusion', \"entity(text='second\", 'chopper']\n",
+      "🧠 Semantica is extracting: Extracted 19 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracting relations using pattern... 🔄🎯 (0.7s)DEBUG: Entity map keys: ['href=\"https://owasp.org', 'china chopper', 'transport layer security', 'linux', 'windows', 'href=\"https://cloud.google.com', 'hypertext preprocessor', 'php', '1543', 'end_char=11131', 'confidence=1.0', 'chopper', \"preprocessor'\", 'end_char=12304']\n",
+      "🧠 Semantica is extracting: Extracted 19 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracting relations using pattern... 🔄🎯 (0.7s)DEBUG: Entity map keys: ['php', '2007', 'mimikatz', 'local security authority subsystem service', 'recent years', '1092', 'confidence=1.0', 'end_char=12962', \"security authority subsystem service'\", 'end_char=13436']\n",
+      "🧠 Semantica is extracting: Extracted 19 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracting relations using pattern... 🔄🎯 (2.7s)DEBUG: Entity map keys: ['mimikatz', 'powershell empire', 'local area network', 'windows xp', '2003', 'powershell', 'invoke-mimikatz', 'several years', '2011', 'diginotar', 'a month', 'notpetya', 'badrabbit', '2017', 'microsoft', 'windows 8.1/server', 'href=\"https://support.microsoft.com', '2014-93434251-04ac', 'target=\"_blank', 'may 13', 'credential guard.</p>\\\\n', '>credential guard', 'windows hardware compatibility program specifications', '2016', 'windows server semi-annual', 'li', 'href=\"https://learn.microsoft.com', 'windows 10', 'windows', '4769', 'end_char=14141', 'end_char=14305', \"entity(text='powershell empire'\", \"area network'\", 'end_char=14911', 'confidence=1.0', 'xp', \"entity(text='powershell\", \"invoke-mimikatz'\", \"several year'\", 'end_char=15614', 'end_char=15736', \"entity(text='notpetya\", 'end_char=15962', \"end_char=16742, confidence=1.0, metadata={'extraction_method\", \"2014-93434251-04ac'\", \"may 13'\", \"label='law\", 'end_char=17635', \"hardware compatibility program specifications'\", \"windows hardware compatibility program specifications'\", \"entity(text='2016'\", \"server semi-annual'\", 'end_char=17759', 'end_char=17817', 'end_char=17919', 'end_char=18149']\n",
+      "🧠 Semantica is extracting: Extracted 19 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracting relations using pattern... 🔄🎯 (1.4s)3.2s)DEBUG: Entity map keys: ['local administrator password solution', 'microsoft', 'windows', 'microsoft advanced threat analytics', 'numerous mimikatz', 'mimikatz', 'wherever mimikatz', '1546', \"password solution'\", 'confidence=1.0', \"advanced threat analytics'\", 'end_char=18856', \"microsoft advanced threat analytics'\", 'start_char=18999', 'end_char=19016', 'end_char=19540']\n",
+      "🧠 Semantica is extracting: Extracted 19 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracting relations using pattern... 🔄🎯 (1.8s)DEBUG: Entity map keys: ['mimikatz', 'active directory', 'securing active', 'powershell', '>lateral movement framework', '1133', 'confidence=1.0', 'directory', \"entity(text='powershell\", 'end_char=20611', 'end_char=20690']\n",
+      "Extracted 176 relationships\n"
+     ]
+    }
+   ],
    "source": [
     "from semantica.semantic_extract import RelationExtractor\n",
-    "from contextlib import redirect_stderr\n",
-    "from io import StringIO\n",
     "\n",
     "relation_extractor = RelationExtractor(\n",
-    "    provider=\"groq\",\n",
-    "    model=\"llama-3.1-8b-instant\"\n",
+    "    method=[\"dependency\", \"pattern\", \"cooccurrence\"],\n",
+    "    model=\"en_core_web_sm\",\n",
+    "    confidence_threshold=0.5,\n",
+    "    max_distance=100\n",
     ")\n",
     "\n",
-    "relation_types = [\n",
-    "    \"associated_with\", \"connected_to\", \"involved_in\",\n",
-    "    \"located_at\", \"related_to\"\n",
-    "]\n",
+    "relevant_types = [\"PERSON\", \"ORG\", \"GPE\", \"LOC\", \"EVENT\", \"DATE\"]\n",
+    "chunk_entities_list = [[e for e in entities if e.label in relevant_types] for entities in entity_results]\n",
+    "relation_results = relation_extractor.extract(chunks_to_process, chunk_entities_list)\n",
     "\n",
     "all_relationships = []\n",
-    "chunks_to_process = chunked_docs[:10]  # Limit for demo\n",
-    "print(f\"Extracting relationships from {len(chunks_to_process)} chunks...\")\n",
-    "for i, chunk in enumerate(chunks_to_process, 1):\n",
-    "    try:\n",
-    "        with redirect_stderr(StringIO()):\n",
-    "            relationships = relation_extractor.extract(\n",
-    "                chunk,\n",
-    "                relation_types=relation_types\n",
-    "            )\n",
-    "            all_relationships.extend(relationships)\n",
-    "    except Exception:\n",
-    "        pass\n",
-    "    \n",
-    "    if i % 5 == 0 or i == len(chunks_to_process):\n",
-    "        print(f\"  Processed {i}/{len(chunks_to_process)} chunks ({len(all_relationships)} relationships found)\")\n",
+    "seen = set()\n",
+    "for relationships in relation_results:\n",
+    "    for rel in relationships:\n",
+    "        key = (rel.subject.text, rel.predicate, rel.object.text)\n",
+    "        if key not in seen:\n",
+    "            seen.add(key)\n",
+    "            all_relationships.append(rel)\n",
     "\n",
     "print(f\"Extracted {len(all_relationships)} relationships\")\n"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "\n",
-    "## Deduplication\n",
-    "\n",
-    "Deduplicate person and organization records to ensure accurate network analysis.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from semantica.kg import EntityResolver\n",
-    "from semantica.semantic_extract import Entity\n",
-    "\n",
-    "# Convert Entity objects to dictionaries for EntityResolver\n",
-    "print(f\"Converting {len(all_entities)} entities to dictionaries...\")\n",
-    "entity_dicts = [{\"name\": e.get(\"name\", e.get(\"text\", \"\")), \"type\": e.get(\"type\", \"\"), \"start_char\": getattr(e, 'start_char', 0), \"end_char\": getattr(e, 'end_char', 0), \"confidence\": e.get(\"confidence\", 1.0)} for e in all_entities]\n",
-    "\n",
-    "# Use EntityResolver class to resolve duplicates\n",
-    "entity_resolver = EntityResolver(strategy=\"fuzzy\", similarity_threshold=0.85)\n",
-    "\n",
-    "print(f\"Resolving duplicates in {len(entity_dicts)} entities...\")\n",
-    "resolved_entities = entity_resolver.resolve_entities(entity_dicts)\n",
-    "\n",
-    "# Convert back to Entity objects\n",
-    "print(f\"Converting {len(resolved_entities)} resolved entities back to Entity objects...\")\n",
-    "merged_entities = [\n",
-    "    Entity(text=e[\"name\"], label=e[\"type\"], start_char=e.get(\"start_char\", 0), end_char=e.get(\"end_char\", 0), confidence=e.get(\"confidence\", 1.0))\n",
-    "    if isinstance(e, dict) else e\n",
-    "    for e in resolved_entities\n",
-    "]\n",
-    "\n",
-    "all_entities = merged_entities\n",
-    "print(f\"Deduplicated {len(entity_dicts)} entities to {len(merged_entities)} unique entities\")\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -403,31 +582,100 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Detecting conflicts in 289 entities...\n",
+      "🧠 Semantica is extracting: Extracting relations using pattern... 🔄🎯 (1.8s) | 🧠 Semantica is resolving: Checking entity groups for conflicts... 26/155 (remaining: 129) |██░░░░░░░░░░░░░| 16.8% [26/155] 🔄⚠️ (ETA: 3.5s | 37.0/s)s)"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Value conflict detected: PII.label has conflicting values: ['PERSON', 'ORG']\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🧠 Semantica is extracting: Extracting relations using pattern... 🔄🎯 (1.8s) | 🧠 Semantica is resolving: Checking entity groups for conflicts... 47/155 (remaining: 108) |████░░░░░░░░░░░| 30.3% [47/155] 🔄⚠️ (ETA: 1.6s | 65.6/s)"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Value conflict detected: China Chopper.label has conflicting values: ['PERSON', 'ORG']\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🧠 Semantica is extracting: Extracting relations using pattern... 🔄🎯 (1.8s) | 🧠 Semantica is resolving: Checking entity groups for conflicts... 105/155 (remaining: 50) |██████████░░░░░| 67.7% [105/155] 🔄⚠️ (ETA: 0.4s | 134.9/s)"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Value conflict detected: href=\"https://learn.microsoft.com.label has conflicting values: ['PERSON', 'ORG']\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🧠 Semantica is extracting: Extracting relations using pattern... 🔄🎯 (1.8s) | 🧠 Semantica is resolving: Detected 0 relationship conflicts |███████████████| 100.0% [0/176] ✅⚠️░| 0.0% [0/176] 🔄⚠️223.9/s) | 223.5/s)).3s | 148.8/s)Detected 3 conflicts\n",
+      "🧠 Semantica is resolving: Detected 0 relationship conflicts |███████████████| 100.0% [0/176] ✅⚠️ | 🧠 Semantica is resolving: Resolving conflicts... 0/3 (remaining: 3) |░░░░░░░░░░░░░░░| 0.0% [0/3] 🔄⚠️Resolved 3 conflicts\n"
+     ]
+    }
+   ],
    "source": [
     "from semantica.conflicts import ConflictDetector, ConflictResolver\n",
     "\n",
-    "# Use value conflict detection for property value disagreements\n",
-    "# credibility_weighted strategy prioritizes authoritative intelligence sources\n",
     "conflict_detector = ConflictDetector()\n",
     "conflict_resolver = ConflictResolver()\n",
     "\n",
-    "print(f\"Detecting value conflicts in {len(all_entities)} entities...\")\n",
-    "conflicts = conflict_detector.detect_conflicts(\n",
-    "    entities=all_entities,\n",
-    "    relationships=all_relationships,\n",
-    "    method=\"value\"  # Detect property value conflicts\n",
-    ")\n",
+    "entity_dicts = [\n",
+    "    {\n",
+    "        \"id\": getattr(e, \"text\", str(e)),\n",
+    "        \"text\": getattr(e, \"text\", str(e)),\n",
+    "        \"label\": getattr(e, \"label\", \"\"),\n",
+    "        \"metadata\": getattr(e, \"metadata\", {})\n",
+    "    }\n",
+    "    for e in all_entities\n",
+    "]\n",
     "\n",
-    "print(f\"Detected {len(conflicts)} value conflicts\")\n",
+    "print(f\"Detecting conflicts in {len(entity_dicts)} entities...\")\n",
+    "conflicts = conflict_detector.detect_entity_conflicts(entity_dicts)\n",
+    "\n",
+    "if all_relationships:\n",
+    "    relationship_dicts = [\n",
+    "        {\n",
+    "            \"source_id\": getattr(rel.subject, \"text\", str(rel.subject)),\n",
+    "            \"target_id\": getattr(rel.object, \"text\", str(rel.object)),\n",
+    "            \"type\": rel.predicate,\n",
+    "            \"confidence\": rel.confidence,\n",
+    "            \"metadata\": rel.metadata\n",
+    "        }\n",
+    "        for rel in all_relationships\n",
+    "    ]\n",
+    "    relationship_conflicts = conflict_detector.detect_relationship_conflicts(relationship_dicts)\n",
+    "    conflicts.extend(relationship_conflicts)\n",
+    "\n",
+    "print(f\"Detected {len(conflicts)} conflicts\")\n",
     "\n",
     "if conflicts:\n",
-    "    print(f\"Resolving conflicts using credibility_weighted strategy...\")\n",
     "    resolved = conflict_resolver.resolve_conflicts(\n",
     "        conflicts,\n",
-    "        strategy=\"credibility_weighted\"  # Intelligence sources have different credibility\n",
+    "        strategy=\"credibility_weighted\"\n",
     "    )\n",
     "    print(f\"Resolved {len(resolved)} conflicts\")\n",
     "else:\n",
@@ -447,9 +695,97 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Building knowledge graph...\n",
+      "🧠 Semantica is resolving: Resolving conflicts... 0/3 (remaining: 3) |░░░░░░░░░░░░░░░| 0.0% [0/3] 🔄⚠️ | 🧠 Semantica is building: Knowledge graph from 289 source(s) 🔄🧠 (0.0s)Building graph structure...\n",
+      "✅ Graph structure built (0.00s)\n",
+      "🧠 Semantica is resolving: Resolving conflicts... 0/3 (remaining: 3) |░░░░░░░░░░░░░░░| 0.0% [0/3] 🔄⚠️ | 🧠 Semantica is building: Knowledge graph from 289 source(s) 🔄🧠 (0.0s)"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Type conflict detected: PII conflicting types: ['PERSON', 'ORG']\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🧠 Semantica is resolving: Resolving conflicts... 0/3 (remaining: 3) |░░░░░░░░░░░░░░░| 0.0% [0/3] 🔄⚠️ | 🧠 Semantica is building: Knowledge graph from 289 source(s) 🔄🧠 (0.0s)"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Type conflict detected: China Chopper conflicting types: ['PERSON', 'ORG']\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🧠 Semantica is resolving: Resolving conflicts... 0/3 (remaining: 3) |░░░░░░░░░░░░░░░| 0.0% [0/3] 🔄⚠️ | 🧠 Semantica is building: Knowledge graph from 289 source(s) 🔄🧠 (0.0s)"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Type conflict detected: href=\"https://learn.microsoft.com conflicting types: ['PERSON', 'ORG']\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🧠 Semantica is resolving: Resolving conflicts... 0/3 (remaining: 3) |░░░░░░░░░░░░░░░| 0.0% [0/3] 🔄⚠️ | 🧠 Semantica is building: Knowledge graph from 289 source(s) 🔄🧠 (0.0s)"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected 3 conflict(s) in graph\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🧠 Semantica is resolving: Resolving conflicts... 0/3 (remaining: 3) |░░░░░░░░░░░░░░░| 0.0% [0/3] 🔄⚠️ | 🧠 Semantica is building: Knowledge graph from 289 source(s) 🔄🧠 (0.0s)"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No conflicts were automatically resolved\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "============================================================\n",
+      "✅ Knowledge Graph Build Complete\n",
+      "   Entities: 289\n",
+      "   Relationships: 176\n",
+      "   Total time: 4.39s\n",
+      "============================================================\n",
+      "Built KG with 289 entities and 176 relationships\n"
+     ]
+    }
+   ],
    "source": [
     "from semantica.kg import GraphBuilder\n",
     "\n",
@@ -457,7 +793,7 @@
     "\n",
     "print(f\"Building knowledge graph...\")\n",
     "kg = builder.build(\n",
-    "    entities=all_entities,\n",
+    "    sources=all_entities,\n",
     "    relationships=all_relationships\n",
     ")\n",
     "\n",
@@ -479,46 +815,41 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "fastembed not available. Install with: pip install fastembed. Using fallback embedding method.\n",
+      "fastembed not available. Install with: pip install fastembed. Using fallback embedding method.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating embeddings for 20 chunks...\n",
+      "  Generated 5/20 embeddings...\n",
+      "  Generated 10/20 embeddings...\n",
+      "  Generated 15/20 embeddings...\n",
+      "  Generated 20/20 embeddings...\n",
+      "Storing 0 embeddings in vector store...\n",
+      "Generated 0 embeddings and stored in vector database\n"
+     ]
+    }
+   ],
    "source": [
     "from semantica.embeddings import EmbeddingGenerator\n",
     "from semantica.vector_store import VectorStore\n",
-    "from contextlib import redirect_stderr\n",
-    "from io import StringIO\n",
     "\n",
-    "embedding_gen = EmbeddingGenerator(\n",
-    "    model_name=EMBEDDING_MODEL,\n",
-    "    dimension=EMBEDDING_DIMENSION\n",
-    ")\n",
+    "embedding_gen = EmbeddingGenerator(model_name=EMBEDDING_MODEL, dimension=EMBEDDING_DIMENSION)\n",
+    "chunks_to_embed = chunked_docs[:20]\n",
     "\n",
-    "# Generate embeddings for chunks\n",
-    "chunks_to_embed = chunked_docs[:20]  # Limit for demo\n",
-    "print(f\"Generating embeddings for {len(chunks_to_embed)} chunks...\")\n",
-    "embeddings = []\n",
-    "for i, chunk in enumerate(chunks_to_embed, 1):\n",
-    "    try:\n",
-    "        with redirect_stderr(StringIO()):\n",
-    "            embedding = embedding_gen.generate(chunk)\n",
-    "            embeddings.append(embedding)\n",
-    "    except Exception:\n",
-    "        pass\n",
-    "    if i % 5 == 0 or i == len(chunks_to_embed):\n",
-    "        print(f\"  Generated {i}/{len(chunks_to_embed)} embeddings...\")\n",
+    "embeddings = embedding_gen.generate_embeddings(chunks_to_embed)\n",
     "\n",
-    "# Create vector store\n",
     "vector_store = VectorStore(backend=\"faiss\", dimension=EMBEDDING_DIMENSION)\n",
-    "\n",
-    "# Add embeddings to vector store\n",
-    "print(f\"Storing {len(embeddings)} embeddings in vector store...\")\n",
     "for i, (chunk, embedding) in enumerate(zip(chunks_to_embed, embeddings)):\n",
-    "    try:\n",
-    "        vector_store.add(\n",
-    "            id=str(i),\n",
-    "            embedding=embedding,\n",
-    "            metadata={\"text\": chunk[:100]}  # Store first 100 chars\n",
-    "        )\n",
-    "    except Exception:\n",
-    "        pass\n",
+    "    vector_store.add(str(i), embedding, {\"text\": chunk[:100]})\n",
     "\n",
     "print(f\"Generated {len(embeddings)} embeddings and stored in vector database\")\n"
    ]
@@ -536,35 +867,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🧠 Semantica is building: Knowledge graph from 289 source(s) 🔄🧠 (0.0s) | 🧠 Semantica is building: Calculating degree centrality 🔄🧠 (0.0s)Top 5 key players: ['confidence=1.0', 'Mimikatz', 'Microsoft', '2016', 'Australia']\n",
+      "Top 5 brokers: ['confidence=1.0', 'Mimikatz', 'Microsoft', \"the United Kingdom National Cyber Security Centre'\", 'PowerShell']\n"
+     ]
+    }
+   ],
    "source": [
     "from semantica.kg import CentralityCalculator\n",
-    "from contextlib import redirect_stderr\n",
-    "from io import StringIO\n",
     "\n",
-    "centrality_calc = CentralityCalculator(kg)\n",
+    "calculator = CentralityCalculator()\n",
+    "all_centrality = calculator.calculate_all_centrality(kg)\n",
     "\n",
-    "try:\n",
-    "    with redirect_stderr(StringIO()):\n",
-    "        # Calculate all centrality measures\n",
-    "        degree_centrality = centrality_calc.degree_centrality()\n",
-    "        betweenness_centrality = centrality_calc.betweenness_centrality()\n",
-    "        closeness_centrality = centrality_calc.closeness_centrality()\n",
-    "        eigenvector_centrality = centrality_calc.eigenvector_centrality()\n",
-    "        \n",
-    "        # Identify key players (high degree centrality)\n",
-    "        if degree_centrality:\n",
-    "            top_players = sorted(degree_centrality.items(), key=lambda x: x[1], reverse=True)[:5]\n",
-    "            print(f\"Top 5 key players by degree centrality: {[p[0] for p in top_players]}\")\n",
-    "        \n",
-    "        # Identify brokers (high betweenness centrality)\n",
-    "        if betweenness_centrality:\n",
-    "            top_brokers = sorted(betweenness_centrality.items(), key=lambda x: x[1], reverse=True)[:5]\n",
-    "            print(f\"Top 5 brokers by betweenness centrality: {[b[0] for b in top_brokers]}\")\n",
-    "except Exception:\n",
-    "    print(\"Centrality analysis completed\")\n"
+    "degree = all_centrality[\"centrality_measures\"][\"degree\"]\n",
+    "betweenness = all_centrality[\"centrality_measures\"][\"betweenness\"]\n",
+    "\n",
+    "print(f\"Top 5 key players: {[p['node'] for p in degree['rankings'][:5]]}\")\n",
+    "print(f\"Top 5 brokers: {[b['node'] for b in betweenness['rankings'][:5]]}\")"
    ]
   },
   {
@@ -580,32 +905,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🧠 Semantica is building: Calculating degree centrality 🔄🧠 (0.0s) | 🧠 Semantica is building: Detecting communities with NetworkX... 🔄🧠 (3.5s) (0.0s)Detected 19 communities\n",
+      "Detected 13 overlapping communities\n"
+     ]
+    }
+   ],
    "source": [
     "from semantica.kg import CommunityDetector\n",
-    "from contextlib import redirect_stderr\n",
-    "from io import StringIO\n",
     "\n",
-    "community_detector = CommunityDetector(kg)\n",
+    "detector = CommunityDetector()\n",
+    "communities = detector.detect_communities(kg, \"louvain\")\n",
+    "overlapping = detector.detect_communities(kg, \"overlapping\")\n",
     "\n",
-    "try:\n",
-    "    with redirect_stderr(StringIO()):\n",
-    "        # Detect communities using Louvain algorithm\n",
-    "        communities = community_detector.detect_communities(method=\"louvain\")\n",
-    "        print(f\"Detected {len(communities)} communities using Louvain algorithm\")\n",
-    "        \n",
-    "        # Detect overlapping communities\n",
-    "        overlapping_communities = community_detector.detect_communities(method=\"overlapping\")\n",
-    "        print(f\"Detected {len(overlapping_communities)} overlapping communities\")\n",
-    "        \n",
-    "        # Analyze community structure\n",
-    "        if communities:\n",
-    "            largest_community = max(communities, key=len)\n",
-    "            print(f\"Largest community has {len(largest_community)} members\")\n",
-    "except Exception:\n",
-    "    print(\"Community detection completed\")\n"
+    "print(f\"Detected {len(communities.get('communities', []))} communities\")\n",
+    "print(f\"Detected {len(overlapping.get('communities', []))} overlapping communities\")\n"
    ]
   },
   {
@@ -621,37 +941,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🧠 Semantica is building: Calculating degree centrality 🔄🧠 (0.0s) | 🧠 Semantica is building: Detecting communities with NetworkX... 🔄🧠 (3.5s)Graph: 125 nodes, 168 edges\n",
+      "Connected components: 13\n"
+     ]
+    }
+   ],
    "source": [
     "from semantica.kg import GraphAnalyzer\n",
-    "from contextlib import redirect_stderr\n",
-    "from io import StringIO\n",
     "\n",
-    "graph_analyzer = GraphAnalyzer(kg)\n",
+    "analyzer = GraphAnalyzer()\n",
+    "results = analyzer.analyze_graph(kg)\n",
     "\n",
-    "try:\n",
-    "    with redirect_stderr(StringIO()):\n",
-    "        # Analyze graph structure\n",
-    "        stats = graph_analyzer.get_statistics()\n",
-    "        print(f\"Graph statistics: {stats.get('num_nodes', 0)} nodes, {stats.get('num_edges', 0)} edges\")\n",
-    "        \n",
-    "        # Find paths between entities\n",
-    "        if all_entities:\n",
-    "            person_entities = [e for e in all_entities if e.get(\"type\") == \"Person\"]\n",
-    "            if len(person_entities) >= 2:\n",
-    "                source = person_entities[0].get(\"name\", \"\")\n",
-    "                target = person_entities[1].get(\"name\", \"\")\n",
-    "                if source and target:\n",
-    "                    paths = graph_analyzer.find_paths(source=source, target=target, max_length=3)\n",
-    "                    print(f\"Found {len(paths)} paths between {source} and {target}\")\n",
-    "        \n",
-    "        # Analyze connectivity\n",
-    "        connectivity = graph_analyzer.analyze_connectivity()\n",
-    "        print(f\"Connectivity analysis: {len(connectivity.get('components', []))} connected components\")\n",
-    "except Exception:\n",
-    "    print(\"Graph analytics completed\")\n"
+    "stats = results.get(\"metrics\", {})\n",
+    "connectivity = results.get(\"connectivity\", {})\n",
+    "\n",
+    "print(f\"Graph: {stats.get('num_nodes', 0)} nodes, {stats.get('num_edges', 0)} edges\")\n",
+    "print(f\"Connected components: {len(connectivity.get('components', []))}\")\n"
    ]
   },
   {
@@ -667,37 +979,169 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🧠 Semantica is indexing: Storing vectors... 🔄📊 (0.0s) | 🧠 Semantica is processing: Building graph from 289 entities and 176 relationships 🔄🔗 (0.0s)Intelligence Context Graph: 155 nodes, 176 edges\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🧠 Semantica is indexing: Stored 1 vectors |███████████████| 100.0% ✅📊 | 🧠 Semantica is processing: Building graph from 289 entities and 176 relationships 🔄🔗 (0.0s)\n",
+      "================================================================================\n",
+      "Criminal Intelligence Analysis - GraphRAG with Multi-Hop Reasoning\n",
+      "================================================================================\n",
+      "\n",
+      "================================================================================\n",
+      "Intelligence Query: Who are the key players and central nodes in the criminal network?\n",
+      "================================================================================\n",
+      "\n",
+      "🧠 Semantica is indexing: Searching for 60 similar vectors 🔄📊 (0.0s) | 🧠 Semantica is processing: Building graph from 289 entities and 176 relationships 🔄🔗 (0.0s))"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Embedding generation failed: Text cannot be empty or whitespace-only\n",
+      "Using random fallback embedding\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generated Response:\n",
+      "Based on the retrieved context, I was unable to identify key players and central nodes in a criminal network. The context appears to be related to the United Kingdom and its National Cyber Security Centre, but it does not provide information about a specific criminal network.\n",
+      "\n",
+      "However, I can infer that the United Kingdom National Cyber Security Centre (Context 2 and Context 3) might be involved in combating cybercrime, which could be related to a criminal network. But without more specific information, it is not possible to determine the key players and central nodes in such a network.\n",
+      "\n",
+      "If I had to make an educated guess, I would say that the key players and central nodes in a criminal network might be individuals or organizations that are involved in cybercrime, such as hackers, cyberterrorists, or organized crime groups. However, this is purely speculative and not based on any information from the retrieved context.\n",
+      "\n",
+      "In terms of multi-hop connections, I did not find any relevant relationships between the entities in the context. The context primarily consists of different mentions of the United Kingdom and its National Cyber Security Centre, but it does not provide any connections between these entities and a criminal network.\n",
+      "\n",
+      "In conclusion, I was unable to provide a definitive answer to the question based on the retrieved context. If you have more information or context about the criminal network, I may be able to provide a more accurate answer.\n",
+      "\n",
+      "Confidence: 0.422\n",
+      "Sources: 15\n",
+      "Reasoning Paths: 0\n",
+      "\n",
+      "\n",
+      "================================================================================\n",
+      "Intelligence Query: What are the operational relationships between criminal organizations?\n",
+      "================================================================================\n",
+      "\n",
+      "🧠 Semantica is indexing: Searching for 60 similar vectors 🔄📊 (0.0s) | 🧠 Semantica is processing: Building graph from 289 entities and 176 relationships 🔄🔗 (0.0s)"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Embedding generation failed: Text cannot be empty or whitespace-only\n",
+      "Using random fallback embedding\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generated Response:\n",
+      "Based on the retrieved context, I couldn't find direct information about the operational relationships between criminal organizations. However, I can provide some insights by making multi-hop connections.\n",
+      "\n",
+      "One possible connection is through the concept of \"Area Network\" (Context 2, Score: 0.43), which can be related to organized crime groups using communication networks to coordinate their activities. For instance, a reasoning path could be:\n",
+      "\n",
+      "Area Network → Communication Network → Organized Crime Group → Operational Relationship\n",
+      "\n",
+      "However, this connection is indirect and requires additional information to establish a clear relationship between criminal organizations.\n",
+      "\n",
+      "Another possible connection is through the concept of \"the United Kingdom\" (Context 4, Score: 0.42), which is related to the United Kingdom National Cyber Security Centre (Context 1, Score: 0.44). While this connection doesn't directly relate to criminal organizations, it could be used to infer that the UK National Cyber Security Centre might be involved in monitoring or disrupting the operational relationships between criminal organizations.\n",
+      "\n",
+      "To answer the question, I would say that while there is no direct information available, there are possible indirect connections that can be made through the concepts of Area Network and the United Kingdom. However, more information is needed to establish a clear understanding of the operational relationships between criminal organizations.\n",
+      "\n",
+      "If I had to provide a more specific answer, I would say that:\n",
+      "\n",
+      "* There is no direct information about the operational relationships between criminal organizations in the retrieved context.\n",
+      "* Possible indirect connections exist through the concepts of Area Network and the United Kingdom, but more information is needed to establish a clear understanding.\n",
+      "* The United Kingdom National Cyber Security Centre might be involved in monitoring or disrupting the operational relationships between criminal organizations, but this is purely speculative and not directly supported by the retrieved context.\n",
+      "\n",
+      "Confidence: 0.274\n",
+      "Sources: 11\n",
+      "Reasoning Paths: 0\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "from semantica.context import AgentContext\n",
-    "from contextlib import redirect_stderr\n",
-    "from io import StringIO\n",
+    "from semantica.context import AgentContext, ContextGraph\n",
+    "from semantica.llms import Groq\n",
+    "import os\n",
     "\n",
-    "agent_context = AgentContext(\n",
-    "    vector_store=vector_store,\n",
-    "    knowledge_graph=kg\n",
+    "context_graph = ContextGraph()\n",
+    "context_graph.build_from_entities_and_relationships(\n",
+    "    entities=kg.get('entities', []),\n",
+    "    relationships=[{**r, 'source_id': r.get('source_id') or r.get('source'), 'target_id': r.get('target_id') or r.get('target')} for r in kg.get('relationships', [])]\n",
     ")\n",
     "\n",
-    "queries = [\n",
-    "    \"Who are the key players in the criminal network?\",\n",
-    "    \"What organizations are connected to person X?\",\n",
-    "    \"What events occurred at location Y?\",\n",
-    "    \"What are the relationships between organization A and organization B?\"\n",
+    "graph_stats = context_graph.stats()\n",
+    "print(f\"Intelligence Context Graph: {graph_stats['node_count']} nodes, {graph_stats['edge_count']} edges\")\n",
+    "\n",
+    "context = AgentContext(\n",
+    "    vector_store=vector_store,\n",
+    "    knowledge_graph=context_graph,\n",
+    "    hybrid_alpha=0.7,\n",
+    "    use_graph_expansion=True,\n",
+    "    max_expansion_hops=3\n",
+    ")\n",
+    "\n",
+    "for chunk in chunked_docs[:30]:\n",
+    "    if chunk and chunk.strip():\n",
+    "        context.store(\n",
+    "            content=chunk,\n",
+    "            metadata={'source': 'criminal_intelligence'},\n",
+    "            extract_entities=True,\n",
+    "            link_entities=True\n",
+    "        )\n",
+    "\n",
+    "llm = Groq(model=\"llama-3.1-8b-instant\", api_key=os.getenv(\"GROQ_API_KEY\"))\n",
+    "\n",
+    "intelligence_queries = [\n",
+    "    \"Who are the key players and central nodes in the criminal network?\",\n",
+    "    \"What are the operational relationships between criminal organizations?\"\n",
     "]\n",
     "\n",
-    "for query in queries:\n",
-    "    try:\n",
-    "        with redirect_stderr(StringIO()):\n",
-    "            results = agent_context.query(\n",
-    "                query=query,\n",
-    "                top_k=5\n",
-    "            )\n",
-    "            print(f\"Query: {query}\")\n",
-    "            print(f\"Found {len(results.get('results', []))} relevant results\")\n",
-    "    except Exception:\n",
-    "        pass\n"
+    "print(\"\\n\" + \"=\" * 80)\n",
+    "print(\"Criminal Intelligence Analysis - GraphRAG with Multi-Hop Reasoning\")\n",
+    "print(\"=\" * 80)\n",
+    "\n",
+    "for query in intelligence_queries:\n",
+    "    print(f\"\\n{'='*80}\")\n",
+    "    print(f\"Intelligence Query: {query}\")\n",
+    "    print(f\"{'='*80}\\n\")\n",
+    "    \n",
+    "    result = context.query_with_reasoning(\n",
+    "        query=query,\n",
+    "        llm_provider=llm,\n",
+    "        max_results=15,\n",
+    "        max_hops=3,\n",
+    "        min_score=0.2\n",
+    "    )\n",
+    "    \n",
+    "    print(f\"Generated Response:\\n{result.get('response', 'No response available')}\\n\")\n",
+    "    \n",
+    "    if result.get('reasoning_path'):\n",
+    "        print(f\"Reasoning Path:\\n{result.get('reasoning_path')}\\n\")\n",
+    "    \n",
+    "    print(f\"Confidence: {result.get('confidence', 0):.3f}\")\n",
+    "    print(f\"Sources: {result.get('num_sources', 0)}\")\n",
+    "    print(f\"Reasoning Paths: {result.get('num_reasoning_paths', 0)}\")\n",
+    "    print()\n"
    ]
   },
   {
@@ -713,26 +1157,7041 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\umap\\__init__.py:9: ImportWarning: Tensorflow not installed; ParametricUMAP will be unavailable\n",
+      "  warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🧠 Semantica is processing: Building graph from 289 entities and 176 relationships 🔄🔗 (0.0s) | 🧠 Semantica is visualizing: Visualization generated: 289 nodes, 176 edges |███████████████| 100.0% ✅📈"
+     ]
+    },
+    {
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+             1,
+             "#f0f921"
+            ]
+           ],
+           "sequentialminus": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
+            ],
+            [
+             0.3333333333333333,
+             "#9c179e"
+            ],
+            [
+             0.4444444444444444,
+             "#bd3786"
+            ],
+            [
+             0.5555555555555556,
+             "#d8576b"
+            ],
+            [
+             0.6666666666666666,
+             "#ed7953"
+            ],
+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ]
+          },
+          "colorway": [
+           "#636efa",
+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#FFA15A",
+           "#19d3f3",
+           "#FF6692",
+           "#B6E880",
+           "#FF97FF",
+           "#FECB52"
+          ],
+          "font": {
+           "color": "#2a3f5f"
+          },
+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Knowledge Graph Network"
+        },
+        "xaxis": {
+         "showgrid": false,
+         "showticklabels": false,
+         "zeroline": false
+        },
+        "yaxis": {
+         "showgrid": false,
+         "showticklabels": false,
+         "zeroline": false
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from semantica.visualization import KGVisualizer\n",
-    "from contextlib import redirect_stderr\n",
-    "from io import StringIO\n",
     "\n",
-    "visualizer = KGVisualizer()\n",
-    "\n",
-    "try:\n",
-    "    with redirect_stderr(StringIO()):\n",
-    "        visualizer.visualize(\n",
-    "            kg,\n",
-    "            output_path=\"criminal_network.html\",\n",
-    "            layout=\"force_directed\"\n",
-    "        )\n",
-    "        print(\"Knowledge graph visualization saved to criminal_network.html\")\n",
-    "except Exception:\n",
-    "    print(\"Visualization completed\")\n"
+    "visualizer = KGVisualizer(layout=\"force\", color_scheme=\"vibrant\")\n",
+    "visualizer.visualize_network(kg, output=\"interactive\")\n"
    ]
   },
   {
@@ -748,36 +8207,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🧠 Semantica is visualizing: Visualization generated: 289 nodes, 176 edges |███████████████| 100.0% ✅📈 | 🧠 Semantica is exporting: Exporting graph to graphml: criminal_network.graphml 🔄💾 (0.0s)Exported knowledge graph in JSON, GraphML, and CSV formats\n"
+     ]
+    }
+   ],
    "source": [
-    "from semantica.export import GraphExporter\n",
-    "from contextlib import redirect_stderr\n",
-    "from io import StringIO\n",
+    "from semantica.export import GraphExporter, JSONExporter, CSVExporter\n",
     "\n",
-    "exporter = GraphExporter()\n",
+    "GraphExporter().export_knowledge_graph(kg, \"criminal_network.graphml\", format=\"graphml\")\n",
+    "JSONExporter().export_knowledge_graph(kg, \"criminal_network.json\")\n",
+    "CSVExporter().export_knowledge_graph(kg, \"criminal_network.csv\")\n",
     "\n",
-    "try:\n",
-    "    with redirect_stderr(StringIO()):\n",
-    "        # Export as JSON\n",
-    "        exporter.export(kg, format=\"json\", output_path=\"criminal_network.json\")\n",
-    "        \n",
-    "        # Export as GraphML\n",
-    "        exporter.export(kg, format=\"graphml\", output_path=\"criminal_network.graphml\")\n",
-    "        \n",
-    "        # Export as CSV (for intelligence reporting)\n",
-    "        exporter.export(kg, format=\"csv\", output_path=\"criminal_network.csv\")\n",
-    "        \n",
-    "        print(\"Exported knowledge graph in JSON, GraphML, and CSV formats\")\n",
-    "except Exception:\n",
-    "    print(\"Export completed\")\n"
+    "print(\"Exported knowledge graph in JSON, GraphML, and CSV formats\")\n"
    ]
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/cookbook/use_cases/intelligence/criminal_network.graphml b/cookbook/use_cases/intelligence/criminal_network.graphml
new file mode 100644
index 00000000..4fe22193
--- /dev/null
+++ b/cookbook/use_cases/intelligence/criminal_network.graphml
@@ -0,0 +1,2163 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<graphml xmlns="http://graphml.graphdrawing.org/xmlns"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns
+         http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd">
+
+  <key id="type" for="node" attr.name="type" attr.type="string"/>
+  <key id="confidence" for="node" attr.name="confidence" attr.type="double"/>
+
+  <graph id="G" edgedefault="directed">
+
+    <node id="Chunk(text='<div>\n">
+      <data key="label">Chunk(text='<div>\n</data>
+      <data key="type">DATE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Australia">
+      <data key="label">Australia</data>
+      <data key="type">GPE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Canada">
+      <data key="label">Canada</data>
+      <data key="type">GPE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="New Zealand">
+      <data key="label">New Zealand</data>
+      <data key="type">GPE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="the United Kingdom">
+      <data key="label">the United Kingdom</data>
+      <data key="type">GPE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="United States.[1][2][3][4][5]</p>\n">
+      <data key="label">United States.[1][2][3][4][5]</p>\n</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="li>Webshell">
+      <data key="label">li>Webshell</data>
+      <data key="type">PERSON</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="China">
+      <data key="label">China</data>
+      <data key="type">GPE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="li>C2 Obfuscation">
+      <data key="label">li>C2 Obfuscation</data>
+      <data key="type">PERSON</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Australia">
+      <data key="label">Australia</data>
+      <data key="type">GPE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Entity(text='Canada">
+      <data key="label">Entity(text='Canada</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="confidence=1.0">
+      <data key="label">confidence=1.0</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Canada">
+      <data key="label">Canada</data>
+      <data key="type">GPE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Entity(text='New Zealand'">
+      <data key="label">Entity(text='New Zealand'</data>
+      <data key="type">GPE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="New Zealand'">
+      <data key="label">New Zealand'</data>
+      <data key="type">GPE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Entity(text='the United Kingdom'">
+      <data key="label">Entity(text='the United Kingdom'</data>
+      <data key="type">GPE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="confidence=1.0">
+      <data key="label">confidence=1.0</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="the United Kingdom'">
+      <data key="label">the United Kingdom'</data>
+      <data key="type">GPE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="end_char=482">
+      <data key="label">end_char=482</data>
+      <data key="type">PERSON</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="confidence=1.0">
+      <data key="label">confidence=1.0</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="China">
+      <data key="label">China</data>
+      <data key="type">GPE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Today">
+      <data key="label">Today</data>
+      <data key="type">DATE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="today">
+      <data key="label">today</data>
+      <data key="type">DATE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="May 2015">
+      <data key="label">May 2015</data>
+      <data key="type">DATE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Frutas">
+      <data key="label">Frutas</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Wireshark">
+      <data key="label">Wireshark</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Advanced Persistent Threat">
+      <data key="label">Advanced Persistent Threat</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="APT">
+      <data key="label">APT</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Quasar">
+      <data key="label">Quasar</data>
+      <data key="type">PERSON</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Java">
+      <data key="label">Java</data>
+      <data key="type">PERSON</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="MAC">
+      <data key="label">MAC</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="PII">
+      <data key="label">PII</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="early 2018">
+      <data key="label">early 2018</data>
+      <data key="type">DATE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="early 2017">
+      <data key="label">early 2017</data>
+      <data key="type">DATE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Society for Worldwide Interbank Financial Telecommunication">
+      <data key="label">Society for Worldwide Interbank Financial Telecommunication</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Windows Registry">
+      <data key="label">Windows Registry</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Task">
+      <data key="label">Task</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="li>Significant">
+      <data key="label">li>Significant</data>
+      <data key="type">PERSON</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="IP">
+      <data key="label">IP</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="end_char=2514">
+      <data key="label">end_char=2514</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Entity(text='May 2015'">
+      <data key="label">Entity(text='May 2015'</data>
+      <data key="type">DATE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="May 2015'">
+      <data key="label">May 2015'</data>
+      <data key="type">DATE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Frutas">
+      <data key="label">Frutas</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="confidence=1.0">
+      <data key="label">confidence=1.0</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="confidence=1.0">
+      <data key="label">confidence=1.0</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="end_char=4132">
+      <data key="label">end_char=4132</data>
+      <data key="type">PERSON</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="APT">
+      <data key="label">APT</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Quasar">
+      <data key="label">Quasar</data>
+      <data key="type">PERSON</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Java">
+      <data key="label">Java</data>
+      <data key="type">PERSON</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Entity(text='MAC">
+      <data key="label">Entity(text='MAC</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="end_char=4752">
+      <data key="label">end_char=4752</data>
+      <data key="type">PERSON</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="MAC">
+      <data key="label">MAC</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="PII">
+      <data key="label">PII</data>
+      <data key="type">PERSON</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Entity(text='early 2018'">
+      <data key="label">Entity(text='early 2018'</data>
+      <data key="type">DATE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="confidence=1.0">
+      <data key="label">confidence=1.0</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Entity(text='early 2017'">
+      <data key="label">Entity(text='early 2017'</data>
+      <data key="type">DATE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Worldwide Interbank Financial Telecommunication'">
+      <data key="label">Worldwide Interbank Financial Telecommunication'</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="confidence=1.0">
+      <data key="label">confidence=1.0</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="Registry">
+      <data key="label">Registry</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="IP">
+      <data key="label">IP</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="The United Kingdom National Cyber Security Centre">
+      <data key="label">The United Kingdom National Cyber Security Centre</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="UK">
+      <data key="label">UK</data>
+      <data key="type">GPE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="China">
+      <data key="label">China</data>
+      <data key="type">GPE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id=">China Chopper">
+      <data key="label">>China Chopper</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="China Chopper">
+      <data key="label">China Chopper</data>
+      <data key="type">PERSON</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id=">China Chopper">
+      <data key="label">>China Chopper</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="China Chopper">
+      <data key="label">China Chopper</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="China Chopper">
+      <data key="label">China Chopper</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="2869">
+      <data key="label">2869</data>
+      <data key="type">DATE</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="United Kingdom National Cyber Security Centre'">
+      <data key="label">United Kingdom National Cyber Security Centre'</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="end_char=7491">
+      <data key="label">end_char=7491</data>
+      <data key="type">PERSON</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="the United Kingdom National Cyber Security Centre'">
+      <data key="label">the United Kingdom National Cyber Security Centre'</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="confidence=1.0">
+      <data key="label">confidence=1.0</data>
+      <data key="type">ORG</data>
+      <data key="confidence">1.0</data>
+    </node>
+    <node id="UK">
+      <data key="label">UK</data>
+      <data key="type">GPE</data>
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+      <data key="confidence">0.6</data>
+    </edge>
+    <edge source="Securing Active" target="PowerShell">
+      <data key="label">related_to</data>
+      <data key="confidence">0.6</data>
+    </edge>
+    <edge source="PowerShell" target="Mimikatz">
+      <data key="label">related_to</data>
+      <data key="confidence">0.6</data>
+    </edge>
+    <edge source=">Lateral Movement Framework" target="PowerShell">
+      <data key="label">related_to</data>
+      <data key="confidence">0.6</data>
+    </edge>
+    <edge source="Mimikatz" target="Directory">
+      <data key="label">related_to</data>
+      <data key="confidence">0.6</data>
+    </edge>
+    <edge source="Entity(text='PowerShell" target="end_char=20611">
+      <data key="label">related_to</data>
+      <data key="confidence">0.6</data>
+    </edge>
+    <edge source="Entity(text='PowerShell" target="confidence=1.0">
+      <data key="label">related_to</data>
+      <data key="confidence">0.6</data>
+    </edge>
+    <edge source="end_char=20611" target="confidence=1.0">
+      <data key="label">related_to</data>
+      <data key="confidence">0.6</data>
+    </edge>
+    <edge source="end_char=20611" target="PowerShell">
+      <data key="label">related_to</data>
+      <data key="confidence">0.6</data>
+    </edge>
+    <edge source="confidence=1.0" target="PowerShell">
+      <data key="label">related_to</data>
+      <data key="confidence">0.6</data>
+    </edge>
+    <edge source="PowerShell" target="end_char=20690">
+      <data key="label">related_to</data>
+      <data key="confidence">0.6</data>
+    </edge>
+    <edge source="end_char=20690" target="Mimikatz">
+      <data key="label">related_to</data>
+      <data key="confidence">0.6</data>
+    </edge>
+  </graph>
+</graphml>
\ No newline at end of file
diff --git a/cookbook/use_cases/intelligence/criminal_network.json b/cookbook/use_cases/intelligence/criminal_network.json
new file mode 100644
index 00000000..4e78aae6
--- /dev/null
+++ b/cookbook/use_cases/intelligence/criminal_network.json
@@ -0,0 +1,4956 @@
+{
+  "entities": [
+    {
+      "id": "Chunk(text='<div>\\n",
+      "name": "Chunk(text='<div>\\n",
+      "type": "DATE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "chunk(text='<div>\\n"
+      }
+    },
+    {
+      "id": "Australia",
+      "name": "Australia",
+      "type": "GPE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Australia"
+      }
+    },
+    {
+      "id": "Canada",
+      "name": "Canada",
+      "type": "GPE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Canada"
+      }
+    },
+    {
+      "id": "New Zealand",
+      "name": "New Zealand",
+      "type": "GPE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "New Zealand"
+      }
+    },
+    {
+      "id": "the United Kingdom",
+      "name": "the United Kingdom",
+      "type": "GPE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "the United Kingdom"
+      }
+    },
+    {
+      "id": "United States.[1][2][3][4][5]</p>\\n",
+      "name": "United States.[1][2][3][4][5]</p>\\n",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "United States.[1][2][3][4][5]</p>\\n"
+      }
+    },
+    {
+      "id": "li>Webshell",
+      "name": "li>Webshell",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "li>Webshell"
+      }
+    },
+    {
+      "id": "China",
+      "name": "China",
+      "type": "GPE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "China"
+      }
+    },
+    {
+      "id": "li>C2 Obfuscation",
+      "name": "li>C2 Obfuscation",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "li>C2 Obfuscation"
+      }
+    },
+    {
+      "id": "Australia",
+      "name": "Australia",
+      "type": "GPE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Australia"
+      }
+    },
+    {
+      "id": "Entity(text='Canada",
+      "name": "Entity(text='Canada",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Entity(text='Canada"
+      }
+    },
+    {
+      "id": "confidence=1.0",
+      "name": "confidence=1.0",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "confidence=1.0"
+      }
+    },
+    {
+      "id": "Canada",
+      "name": "Canada",
+      "type": "GPE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Canada"
+      }
+    },
+    {
+      "id": "Entity(text='New Zealand'",
+      "name": "Entity(text='New Zealand'",
+      "type": "GPE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Entity(text='New Zealand'"
+      }
+    },
+    {
+      "id": "New Zealand'",
+      "name": "New Zealand'",
+      "type": "GPE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "New Zealand'"
+      }
+    },
+    {
+      "id": "Entity(text='the United Kingdom'",
+      "name": "Entity(text='the United Kingdom'",
+      "type": "GPE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "entity(text='the United Kingdom'"
+      }
+    },
+    {
+      "id": "confidence=1.0",
+      "name": "confidence=1.0",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "confidence=1.0"
+      }
+    },
+    {
+      "id": "the United Kingdom'",
+      "name": "the United Kingdom'",
+      "type": "GPE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "the United Kingdom'"
+      }
+    },
+    {
+      "id": "end_char=482",
+      "name": "end_char=482",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "end_char=482"
+      }
+    },
+    {
+      "id": "confidence=1.0",
+      "name": "confidence=1.0",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "confidence=1.0"
+      }
+    },
+    {
+      "id": "China",
+      "name": "China",
+      "type": "GPE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "China"
+      }
+    },
+    {
+      "id": "Today",
+      "name": "Today",
+      "type": "DATE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "today"
+      }
+    },
+    {
+      "id": "today",
+      "name": "today",
+      "type": "DATE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "today"
+      }
+    },
+    {
+      "id": "May 2015",
+      "name": "May 2015",
+      "type": "DATE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "May 2015"
+      }
+    },
+    {
+      "id": "Frutas",
+      "name": "Frutas",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Frutas"
+      }
+    },
+    {
+      "id": "Wireshark",
+      "name": "Wireshark",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Wireshark"
+      }
+    },
+    {
+      "id": "Advanced Persistent Threat",
+      "name": "Advanced Persistent Threat",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Advanced Persistent Threat"
+      }
+    },
+    {
+      "id": "APT",
+      "name": "APT",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "APT"
+      }
+    },
+    {
+      "id": "Quasar",
+      "name": "Quasar",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Quasar"
+      }
+    },
+    {
+      "id": "Java",
+      "name": "Java",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Java"
+      }
+    },
+    {
+      "id": "MAC",
+      "name": "MAC",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "MAC"
+      }
+    },
+    {
+      "id": "PII",
+      "name": "PII",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "PII"
+      }
+    },
+    {
+      "id": "early 2018",
+      "name": "early 2018",
+      "type": "DATE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "early 2018"
+      }
+    },
+    {
+      "id": "early 2017",
+      "name": "early 2017",
+      "type": "DATE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "early 2017"
+      }
+    },
+    {
+      "id": "Society for Worldwide Interbank Financial Telecommunication",
+      "name": "Society for Worldwide Interbank Financial Telecommunication",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Society for Worldwide Interbank Financial Telecommunication"
+      }
+    },
+    {
+      "id": "Windows Registry",
+      "name": "Windows Registry",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Windows Registry"
+      }
+    },
+    {
+      "id": "Task",
+      "name": "Task",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Task"
+      }
+    },
+    {
+      "id": "li>Significant",
+      "name": "li>Significant",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "li>significant"
+      }
+    },
+    {
+      "id": "IP",
+      "name": "IP",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "IP"
+      }
+    },
+    {
+      "id": "end_char=2514",
+      "name": "end_char=2514",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "end_char=2514"
+      }
+    },
+    {
+      "id": "Entity(text='May 2015'",
+      "name": "Entity(text='May 2015'",
+      "type": "DATE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Entity(text='May 2015'"
+      }
+    },
+    {
+      "id": "May 2015'",
+      "name": "May 2015'",
+      "type": "DATE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "May 2015'"
+      }
+    },
+    {
+      "id": "Frutas",
+      "name": "Frutas",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "fruta"
+      }
+    },
+    {
+      "id": "confidence=1.0",
+      "name": "confidence=1.0",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "confidence=1.0"
+      }
+    },
+    {
+      "id": "confidence=1.0",
+      "name": "confidence=1.0",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "confidence=1.0"
+      }
+    },
+    {
+      "id": "end_char=4132",
+      "name": "end_char=4132",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "end_char=4132"
+      }
+    },
+    {
+      "id": "APT",
+      "name": "APT",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "APT"
+      }
+    },
+    {
+      "id": "Quasar",
+      "name": "Quasar",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Quasar"
+      }
+    },
+    {
+      "id": "Java",
+      "name": "Java",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Java"
+      }
+    },
+    {
+      "id": "Entity(text='MAC",
+      "name": "Entity(text='MAC",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Entity(text='MAC"
+      }
+    },
+    {
+      "id": "end_char=4752",
+      "name": "end_char=4752",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "end_char=4752"
+      }
+    },
+    {
+      "id": "MAC",
+      "name": "MAC",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "MAC"
+      }
+    },
+    {
+      "id": "PII",
+      "name": "PII",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "PII"
+      }
+    },
+    {
+      "id": "Entity(text='early 2018'",
+      "name": "Entity(text='early 2018'",
+      "type": "DATE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "entity(text='early 2018'"
+      }
+    },
+    {
+      "id": "confidence=1.0",
+      "name": "confidence=1.0",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "confidence=1.0"
+      }
+    },
+    {
+      "id": "Entity(text='early 2017'",
+      "name": "Entity(text='early 2017'",
+      "type": "DATE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "entity(text='early 2017'"
+      }
+    },
+    {
+      "id": "Worldwide Interbank Financial Telecommunication'",
+      "name": "Worldwide Interbank Financial Telecommunication'",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Worldwide Interbank Financial Telecommunication'"
+      }
+    },
+    {
+      "id": "confidence=1.0",
+      "name": "confidence=1.0",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "confidence=1.0"
+      }
+    },
+    {
+      "id": "Registry",
+      "name": "Registry",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Registry"
+      }
+    },
+    {
+      "id": "IP",
+      "name": "IP",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "IP"
+      }
+    },
+    {
+      "id": "The United Kingdom National Cyber Security Centre",
+      "name": "The United Kingdom National Cyber Security Centre",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "the United Kingdom National Cyber Security Centre"
+      }
+    },
+    {
+      "id": "UK",
+      "name": "UK",
+      "type": "GPE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "UK"
+      }
+    },
+    {
+      "id": "China",
+      "name": "China",
+      "type": "GPE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "China"
+      }
+    },
+    {
+      "id": ">China Chopper",
+      "name": ">China Chopper",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": ">China Chopper"
+      }
+    },
+    {
+      "id": "China Chopper",
+      "name": "China Chopper",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "China Chopper"
+      }
+    },
+    {
+      "id": ">China Chopper",
+      "name": ">China Chopper",
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+    {
+      "id": "Mimikatz",
+      "name": "Mimikatz",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Mimikatz"
+      }
+    },
+    {
+      "id": "PowerShell Empire",
+      "name": "PowerShell Empire",
+      "type": "GPE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "PowerShell Empire"
+      }
+    },
+    {
+      "id": "Local Area Network",
+      "name": "Local Area Network",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Local Area Network"
+      }
+    },
+    {
+      "id": "Windows XP",
+      "name": "Windows XP",
+      "type": "LOC",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Windows XP"
+      }
+    },
+    {
+      "id": "2003",
+      "name": "2003",
+      "type": "DATE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "2003"
+      }
+    },
+    {
+      "id": "Mimikatz",
+      "name": "Mimikatz",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Mimikatz"
+      }
+    },
+    {
+      "id": "PowerShell",
+      "name": "PowerShell",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "PowerShell"
+      }
+    },
+    {
+      "id": "Invoke-Mimikatz",
+      "name": "Invoke-Mimikatz",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Invoke-Mimikatz"
+      }
+    },
+    {
+      "id": "Mimikatz",
+      "name": "Mimikatz",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Mimikatz"
+      }
+    },
+    {
+      "id": "several years",
+      "name": "several years",
+      "type": "DATE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "several year"
+      }
+    },
+    {
+      "id": "2011",
+      "name": "2011",
+      "type": "DATE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "2011"
+      }
+    },
+    {
+      "id": "DigiNotar",
+      "name": "DigiNotar",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "DigiNotar"
+      }
+    },
+    {
+      "id": "a month",
+      "name": "a month",
+      "type": "DATE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "a month"
+      }
+    },
+    {
+      "id": "Mimikatz",
+      "name": "Mimikatz",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Mimikatz"
+      }
+    },
+    {
+      "id": "NotPetya",
+      "name": "NotPetya",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "NotPetya"
+      }
+    },
+    {
+      "id": "BadRabbit",
+      "name": "BadRabbit",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "BadRabbit"
+      }
+    },
+    {
+      "id": "2017",
+      "name": "2017",
+      "type": "DATE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "2017"
+      }
+    },
+    {
+      "id": "Microsoft",
+      "name": "Microsoft",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Microsoft"
+      }
+    },
+    {
+      "id": "Mimikatz",
+      "name": "Mimikatz",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Mimikatz"
+      }
+    },
+    {
+      "id": "Mimikatz",
+      "name": "Mimikatz",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
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+        "model": "en_core_web_sm",
+        "lemma": "Mimikatz"
+      }
+    },
+    {
+      "id": "Mimikatz",
+      "name": "Mimikatz",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
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+        "model": "en_core_web_sm",
+        "lemma": "Mimikatz"
+      }
+    },
+    {
+      "id": "Microsoft",
+      "name": "Microsoft",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Microsoft"
+      }
+    },
+    {
+      "id": "Mimikatz",
+      "name": "Mimikatz",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
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+        "model": "en_core_web_sm",
+        "lemma": "Mimikatz"
+      }
+    },
+    {
+      "id": "Windows 8.1/Server",
+      "name": "Windows 8.1/Server",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Windows 8.1/Server"
+      }
+    },
+    {
+      "id": "href=\"https://support.microsoft.com",
+      "name": "href=\"https://support.microsoft.com",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "href=\"https://support.microsoft.com"
+      }
+    },
+    {
+      "id": "2014-93434251-04ac",
+      "name": "2014-93434251-04ac",
+      "type": "DATE",
+      "confidence": 1.0,
+      "metadata": {
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+        "model": "en_core_web_sm",
+        "lemma": "2014-93434251-04ac"
+      }
+    },
+    {
+      "id": "target=\"_blank",
+      "name": "target=\"_blank",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "target=\"_blank"
+      }
+    },
+    {
+      "id": "May 13",
+      "name": "May 13",
+      "type": "DATE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "May 13"
+      }
+    },
+    {
+      "id": "Credential Guard.</p>\\n",
+      "name": "Credential Guard.</p>\\n",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Credential Guard.</p>\\n"
+      }
+    },
+    {
+      "id": ">Credential Guard",
+      "name": ">Credential Guard",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": ">Credential Guard"
+      }
+    },
+    {
+      "id": "Microsoft",
+      "name": "Microsoft",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Microsoft"
+      }
+    },
+    {
+      "id": "Windows Hardware Compatibility Program Specifications",
+      "name": "Windows Hardware Compatibility Program Specifications",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Windows Hardware Compatibility Program Specifications"
+      }
+    },
+    {
+      "id": "2016",
+      "name": "2016",
+      "type": "DATE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "2016"
+      }
+    },
+    {
+      "id": "Windows Server Semi-Annual",
+      "name": "Windows Server Semi-Annual",
+      "type": "LOC",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Windows Server Semi-annual"
+      }
+    },
+    {
+      "id": "li",
+      "name": "li",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "li"
+      }
+    },
+    {
+      "id": "Microsoft",
+      "name": "Microsoft",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Microsoft"
+      }
+    },
+    {
+      "id": "href=\"https://learn.microsoft.com",
+      "name": "href=\"https://learn.microsoft.com",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "href=\"https://learn.microsoft.com"
+      }
+    },
+    {
+      "id": "Windows 10",
+      "name": "Windows 10",
+      "type": "LOC",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Windows 10"
+      }
+    },
+    {
+      "id": "Windows",
+      "name": "Windows",
+      "type": "LOC",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Windows"
+      }
+    },
+    {
+      "id": "4769",
+      "name": "4769",
+      "type": "DATE",
+      "confidence": 1.0,
+      "metadata": {
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+        "model": "en_core_web_sm",
+        "lemma": "4769"
+      }
+    },
+    {
+      "id": "Mimikatz",
+      "name": "Mimikatz",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Mimikatz"
+      }
+    },
+    {
+      "id": "end_char=14141",
+      "name": "end_char=14141",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
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+        "model": "en_core_web_sm",
+        "lemma": "end_char=14141"
+      }
+    },
+    {
+      "id": "Mimikatz",
+      "name": "Mimikatz",
+      "type": "PERSON",
+      "confidence": 1.0,
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+        "model": "en_core_web_sm",
+        "lemma": "Mimikatz"
+      }
+    },
+    {
+      "id": "end_char=14305",
+      "name": "end_char=14305",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
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+        "model": "en_core_web_sm",
+        "lemma": "end_char=14305"
+      }
+    },
+    {
+      "id": "Mimikatz",
+      "name": "Mimikatz",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
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+        "model": "en_core_web_sm",
+        "lemma": "Mimikatz"
+      }
+    },
+    {
+      "id": "Mimikatz",
+      "name": "Mimikatz",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
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+        "model": "en_core_web_sm",
+        "lemma": "Mimikatz"
+      }
+    },
+    {
+      "id": "Entity(text='PowerShell Empire'",
+      "name": "Entity(text='PowerShell Empire'",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Entity(text='PowerShell Empire'"
+      }
+    },
+    {
+      "id": "Area Network'",
+      "name": "Area Network'",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Area Network'"
+      }
+    },
+    {
+      "id": "end_char=14911",
+      "name": "end_char=14911",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
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+        "model": "en_core_web_sm",
+        "lemma": "end_char=14911"
+      }
+    },
+    {
+      "id": "confidence=1.0",
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+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
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+        "model": "en_core_web_sm",
+        "lemma": "confidence=1.0"
+      }
+    },
+    {
+      "id": "XP",
+      "name": "XP",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "XP"
+      }
+    },
+    {
+      "id": "confidence=1.0",
+      "name": "confidence=1.0",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
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+        "model": "en_core_web_sm",
+        "lemma": "confidence=1.0"
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+    },
+    {
+      "id": "confidence=1.0",
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+      "type": "ORG",
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+        "model": "en_core_web_sm",
+        "lemma": "confidence=1.0"
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+    },
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+      "id": "2003",
+      "name": "2003",
+      "type": "DATE",
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+        "model": "en_core_web_sm",
+        "lemma": "2003"
+      }
+    },
+    {
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+      "name": "Mimikatz",
+      "type": "PERSON",
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+      "metadata": {
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+        "model": "en_core_web_sm",
+        "lemma": "Mimikatz"
+      }
+    },
+    {
+      "id": "Entity(text='PowerShell",
+      "name": "Entity(text='PowerShell",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Entity(text='PowerShell"
+      }
+    },
+    {
+      "id": "PowerShell",
+      "name": "PowerShell",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "PowerShell"
+      }
+    },
+    {
+      "id": "Invoke-Mimikatz'",
+      "name": "Invoke-Mimikatz'",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Invoke-Mimikatz'"
+      }
+    },
+    {
+      "id": "confidence=1.0",
+      "name": "confidence=1.0",
+      "type": "ORG",
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+      "metadata": {
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+        "model": "en_core_web_sm",
+        "lemma": "confidence=1.0"
+      }
+    },
+    {
+      "id": "Mimikatz",
+      "name": "Mimikatz",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
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+        "model": "en_core_web_sm",
+        "lemma": "Mimikatz"
+      }
+    },
+    {
+      "id": "confidence=1.0",
+      "name": "confidence=1.0",
+      "type": "ORG",
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+      "metadata": {
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+        "model": "en_core_web_sm",
+        "lemma": "confidence=1.0"
+      }
+    },
+    {
+      "id": "several year'",
+      "name": "several year'",
+      "type": "DATE",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "several year'"
+      }
+    },
+    {
+      "id": "end_char=15614",
+      "name": "end_char=15614",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
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+        "model": "en_core_web_sm",
+        "lemma": "end_char=15614"
+      }
+    },
+    {
+      "id": "confidence=1.0",
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+      "confidence": 1.0,
+      "metadata": {
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+        "model": "en_core_web_sm",
+        "lemma": "confidence=1.0"
+      }
+    },
+    {
+      "id": "end_char=15736",
+      "name": "end_char=15736",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
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+        "model": "en_core_web_sm",
+        "lemma": "end_char=15736"
+      }
+    },
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+        "model": "en_core_web_sm",
+        "lemma": "confidence=1.0"
+      }
+    },
+    {
+      "id": "Mimikatz",
+      "name": "Mimikatz",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Mimikatz"
+      }
+    },
+    {
+      "id": "Entity(text='NotPetya",
+      "name": "Entity(text='NotPetya",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "Entity(text='NotPetya"
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+    },
+    {
+      "id": "confidence=1.0",
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+      "type": "ORG",
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+        "model": "en_core_web_sm",
+        "lemma": "confidence=1.0"
+      }
+    },
+    {
+      "id": "NotPetya",
+      "name": "NotPetya",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "NotPetya"
+      }
+    },
+    {
+      "id": "end_char=15962",
+      "name": "end_char=15962",
+      "type": "PERSON",
+      "confidence": 1.0,
+      "metadata": {
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+        "model": "en_core_web_sm",
+        "lemma": "end_char=15962"
+      }
+    },
+    {
+      "id": "BadRabbit",
+      "name": "BadRabbit",
+      "type": "ORG",
+      "confidence": 1.0,
+      "metadata": {
+        "extraction_method": "ml",
+        "model": "en_core_web_sm",
+        "lemma": "BadRabbit"
+      }
+    },
+    {
+      "id": "2017",
+      "name": "2017",
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+        "extraction_method": "co_occurrence",
+        "distance": 2
+      }
+    },
+    {
+      "source": "start_char=18999",
+      "target": "confidence=1.0",
+      "type": "related_to",
+      "confidence": 0.6,
+      "metadata": {
+        "extraction_method": "co_occurrence",
+        "distance": 18
+      }
+    },
+    {
+      "source": "end_char=19016",
+      "target": "confidence=1.0",
+      "type": "related_to",
+      "confidence": 0.6,
+      "metadata": {
+        "extraction_method": "co_occurrence",
+        "distance": 2
+      }
+    },
+    {
+      "source": "Mimikatz",
+      "target": "Mimikatz",
+      "type": "related_to",
+      "confidence": 0.6,
+      "metadata": {
+        "extraction_method": "co_occurrence",
+        "distance": 27
+      }
+    },
+    {
+      "source": "Mimikatz",
+      "target": "end_char=19540",
+      "type": "related_to",
+      "confidence": 0.6,
+      "metadata": {
+        "extraction_method": "co_occurrence",
+        "distance": 72
+      }
+    },
+    {
+      "source": "end_char=19540",
+      "target": "confidence=1.0",
+      "type": "related_to",
+      "confidence": 0.6,
+      "metadata": {
+        "extraction_method": "co_occurrence",
+        "distance": 2
+      }
+    },
+    {
+      "source": "Securing Active",
+      "target": "PowerShell",
+      "type": "related_to",
+      "confidence": 0.6,
+      "metadata": {
+        "extraction_method": "co_occurrence",
+        "distance": 91
+      }
+    },
+    {
+      "source": "PowerShell",
+      "target": "Mimikatz",
+      "type": "related_to",
+      "confidence": 0.6,
+      "metadata": {
+        "extraction_method": "co_occurrence",
+        "distance": 71
+      }
+    },
+    {
+      "source": ">Lateral Movement Framework",
+      "target": "PowerShell",
+      "type": "related_to",
+      "confidence": 0.6,
+      "metadata": {
+        "extraction_method": "co_occurrence",
+        "distance": 2
+      }
+    },
+    {
+      "source": "Mimikatz",
+      "target": "Directory",
+      "type": "related_to",
+      "confidence": 0.6,
+      "metadata": {
+        "extraction_method": "co_occurrence",
+        "distance": 25
+      }
+    },
+    {
+      "source": "Entity(text='PowerShell",
+      "target": "end_char=20611",
+      "type": "related_to",
+      "confidence": 0.6,
+      "metadata": {
+        "extraction_method": "co_occurrence",
+        "distance": 34
+      }
+    },
+    {
+      "source": "Entity(text='PowerShell",
+      "target": "confidence=1.0",
+      "type": "related_to",
+      "confidence": 0.6,
+      "metadata": {
+        "extraction_method": "co_occurrence",
+        "distance": 50
+      }
+    },
+    {
+      "source": "end_char=20611",
+      "target": "confidence=1.0",
+      "type": "related_to",
+      "confidence": 0.6,
+      "metadata": {
+        "extraction_method": "co_occurrence",
+        "distance": 2
+      }
+    },
+    {
+      "source": "end_char=20611",
+      "target": "PowerShell",
+      "type": "related_to",
+      "confidence": 0.6,
+      "metadata": {
+        "extraction_method": "co_occurrence",
+        "distance": 92
+      }
+    },
+    {
+      "source": "confidence=1.0",
+      "target": "PowerShell",
+      "type": "related_to",
+      "confidence": 0.6,
+      "metadata": {
+        "extraction_method": "co_occurrence",
+        "distance": 76
+      }
+    },
+    {
+      "source": "PowerShell",
+      "target": "end_char=20690",
+      "type": "related_to",
+      "confidence": 0.6,
+      "metadata": {
+        "extraction_method": "co_occurrence",
+        "distance": 63
+      }
+    },
+    {
+      "source": "end_char=20690",
+      "target": "Mimikatz",
+      "type": "related_to",
+      "confidence": 0.6,
+      "metadata": {
+        "extraction_method": "co_occurrence",
+        "distance": 92
+      }
+    }
+  ],
+  "nodes": [],
+  "edges": [],
+  "metadata": {
+    "exported_at": "2025-12-30T18:16:22.478658",
+    "num_entities": 289,
+    "num_relationships": 176,
+    "temporal_enabled": false,
+    "timestamp": "2025-12-30T16:55:17.999740",
+    "entity_resolution_applied": false,
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/cookbook/use_cases/intelligence/criminal_network_entities.csv b/cookbook/use_cases/intelligence/criminal_network_entities.csv
new file mode 100644
index 00000000..32691bf6
--- /dev/null
+++ b/cookbook/use_cases/intelligence/criminal_network_entities.csv
@@ -0,0 +1,290 @@
+confidence,end,id,metadata,start,text,type
+1.0,,Chunk(text='<div>\n,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""chunk(text='<div>\\n""}",,Chunk(text='<div>\n,DATE
+1.0,,Australia,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Australia""}",,Australia,GPE
+1.0,,Canada,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Canada""}",,Canada,GPE
+1.0,,New Zealand,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""New Zealand""}",,New Zealand,GPE
+1.0,,the United Kingdom,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""the United Kingdom""}",,the United Kingdom,GPE
+1.0,,United States.[1][2][3][4][5]</p>\n,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""United States.[1][2][3][4][5]</p>\\n""}",,United States.[1][2][3][4][5]</p>\n,ORG
+1.0,,li>Webshell,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""li>Webshell""}",,li>Webshell,PERSON
+1.0,,China,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""China""}",,China,GPE
+1.0,,li>C2 Obfuscation,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""li>C2 Obfuscation""}",,li>C2 Obfuscation,PERSON
+1.0,,Australia,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Australia""}",,Australia,GPE
+1.0,,Entity(text='Canada,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Entity(text='Canada""}",,Entity(text='Canada,ORG
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,Canada,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Canada""}",,Canada,GPE
+1.0,,Entity(text='New Zealand',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Entity(text='New Zealand'""}",,Entity(text='New Zealand',GPE
+1.0,,New Zealand',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""New Zealand'""}",,New Zealand',GPE
+1.0,,Entity(text='the United Kingdom',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""entity(text='the United Kingdom'""}",,Entity(text='the United Kingdom',GPE
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,the United Kingdom',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""the United Kingdom'""}",,the United Kingdom',GPE
+1.0,,end_char=482,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=482""}",,end_char=482,PERSON
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,China,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""China""}",,China,GPE
+1.0,,Today,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""today""}",,Today,DATE
+1.0,,today,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""today""}",,today,DATE
+1.0,,May 2015,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""May 2015""}",,May 2015,DATE
+1.0,,Frutas,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Frutas""}",,Frutas,ORG
+1.0,,Wireshark,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Wireshark""}",,Wireshark,ORG
+1.0,,Advanced Persistent Threat,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Advanced Persistent Threat""}",,Advanced Persistent Threat,ORG
+1.0,,APT,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""APT""}",,APT,ORG
+1.0,,Quasar,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Quasar""}",,Quasar,PERSON
+1.0,,Java,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Java""}",,Java,PERSON
+1.0,,MAC,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""MAC""}",,MAC,ORG
+1.0,,PII,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""PII""}",,PII,ORG
+1.0,,early 2018,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""early 2018""}",,early 2018,DATE
+1.0,,early 2017,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""early 2017""}",,early 2017,DATE
+1.0,,Society for Worldwide Interbank Financial Telecommunication,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Society for Worldwide Interbank Financial Telecommunication""}",,Society for Worldwide Interbank Financial Telecommunication,ORG
+1.0,,Windows Registry,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Windows Registry""}",,Windows Registry,ORG
+1.0,,Task,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Task""}",,Task,ORG
+1.0,,li>Significant,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""li>significant""}",,li>Significant,PERSON
+1.0,,IP,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""IP""}",,IP,ORG
+1.0,,end_char=2514,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=2514""}",,end_char=2514,ORG
+1.0,,Entity(text='May 2015',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Entity(text='May 2015'""}",,Entity(text='May 2015',DATE
+1.0,,May 2015',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""May 2015'""}",,May 2015',DATE
+1.0,,Frutas,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""fruta""}",,Frutas,ORG
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,end_char=4132,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=4132""}",,end_char=4132,PERSON
+1.0,,APT,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""APT""}",,APT,ORG
+1.0,,Quasar,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Quasar""}",,Quasar,PERSON
+1.0,,Java,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Java""}",,Java,PERSON
+1.0,,Entity(text='MAC,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Entity(text='MAC""}",,Entity(text='MAC,ORG
+1.0,,end_char=4752,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=4752""}",,end_char=4752,PERSON
+1.0,,MAC,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""MAC""}",,MAC,ORG
+1.0,,PII,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""PII""}",,PII,PERSON
+1.0,,Entity(text='early 2018',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""entity(text='early 2018'""}",,Entity(text='early 2018',DATE
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,Entity(text='early 2017',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""entity(text='early 2017'""}",,Entity(text='early 2017',DATE
+1.0,,Worldwide Interbank Financial Telecommunication',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Worldwide Interbank Financial Telecommunication'""}",,Worldwide Interbank Financial Telecommunication',ORG
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,Registry,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Registry""}",,Registry,ORG
+1.0,,IP,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""IP""}",,IP,ORG
+1.0,,The United Kingdom National Cyber Security Centre,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""the United Kingdom National Cyber Security Centre""}",,The United Kingdom National Cyber Security Centre,ORG
+1.0,,UK,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""UK""}",,UK,GPE
+1.0,,China,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""China""}",,China,GPE
+1.0,,>China Chopper,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": "">China Chopper""}",,>China Chopper,ORG
+1.0,,China Chopper,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""China Chopper""}",,China Chopper,PERSON
+1.0,,>China Chopper,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": "">China Chopper""}",,>China Chopper,ORG
+1.0,,China Chopper,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""China Chopper""}",,China Chopper,ORG
+1.0,,China Chopper,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""China Chopper""}",,China Chopper,ORG
+1.0,,2869,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""2869""}",,2869,DATE
+1.0,,United Kingdom National Cyber Security Centre',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""United Kingdom National Cyber Security Centre'""}",,United Kingdom National Cyber Security Centre',ORG
+1.0,,end_char=7491,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=7491""}",,end_char=7491,PERSON
+1.0,,the United Kingdom National Cyber Security Centre',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""the United Kingdom National Cyber Security Centre'""}",,the United Kingdom National Cyber Security Centre',ORG
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,UK,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""UK""}",,UK,GPE
+1.0,,China,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""China""}",,China,GPE
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,Chopper,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Chopper""}",,Chopper,PERSON
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,Entity(text='>China Chopper',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Entity(text='>China Chopper'""}",,Entity(text='>China Chopper',PERSON
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,Chopper,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Chopper""}",,Chopper,PERSON
+1.0,,Chopper,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Chopper""}",,Chopper,PERSON
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,summer 2018,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""summer 2018""}",,summer 2018,DATE
+1.0,,CVE-2017-3066,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""CVE-2017-3066""}",,CVE-2017-3066,ORG
+1.0,,Adobe ColdFusion,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Adobe ColdFusion""}",,Adobe ColdFusion,ORG
+1.0,,China Chopper,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""China Chopper""}",,China Chopper,ORG
+1.0,,1327,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""1327""}",,1327,DATE
+1.0,,2018,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""2018""}",,2018,DATE
+1.0,,summer 2018',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""summer 2018'""}",,summer 2018',DATE
+1.0,,Entity(text='CVE-2017-3066',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Entity(text='CVE-2017-3066'""}",,Entity(text='CVE-2017-3066',DATE
+1.0,,ColdFusion,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""ColdFusion""}",,ColdFusion,ORG
+1.0,,Entity(text='second,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Entity(text='second""}",,Entity(text='second,PERSON
+1.0,,Chopper,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Chopper""}",,Chopper,PERSON
+1.0,,"href=""https://owasp.org","{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""href=\""https://owasp.org""}",,"href=""https://owasp.org",ORG
+1.0,,China Chopper,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""China Chopper""}",,China Chopper,ORG
+1.0,,Transport Layer Security,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Transport Layer Security""}",,Transport Layer Security,ORG
+1.0,,China Chopper,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""China Chopper""}",,China Chopper,ORG
+1.0,,China Chopper,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""China Chopper""}",,China Chopper,ORG
+1.0,,Linux,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Linux""}",,Linux,PERSON
+1.0,,Windows,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Windows""}",,Windows,LOC
+1.0,,"href=""https://cloud.google.com","{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""href=\""https://cloud.google.com""}",,"href=""https://cloud.google.com",ORG
+1.0,,Hypertext Preprocessor,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Hypertext Preprocessor""}",,Hypertext Preprocessor,PERSON
+1.0,,PHP,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""PHP""}",,PHP,ORG
+1.0,,1543,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""1543""}",,1543,DATE
+1.0,,end_char=11131,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=11131""}",,end_char=11131,ORG
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,Chopper,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Chopper""}",,Chopper,PERSON
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,Chopper,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Chopper""}",,Chopper,PERSON
+1.0,,Chopper,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Chopper""}",,Chopper,PERSON
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,"href=""https://cloud.google.com","{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""href=\""https://cloud.google.com""}",,"href=""https://cloud.google.com",ORG
+1.0,,Preprocessor',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Preprocessor'""}",,Preprocessor',ORG
+1.0,,end_char=12304,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=12304""}",,end_char=12304,ORG
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,PHP,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""PHP""}",,PHP,ORG
+1.0,,PHP,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""PHP""}",,PHP,ORG
+1.0,,2007,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""2007""}",,2007,DATE
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,Local Security Authority Subsystem Service,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Local Security Authority Subsystem Service""}",,Local Security Authority Subsystem Service,ORG
+1.0,,recent years,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""recent year""}",,recent years,DATE
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,1092,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""1092""}",,1092,DATE
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,PHP,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""PHP""}",,PHP,ORG
+1.0,,2007,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""2007""}",,2007,DATE
+1.0,,end_char=12962,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=12962""}",,end_char=12962,PERSON
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,Security Authority Subsystem Service',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Security Authority Subsystem Service'""}",,Security Authority Subsystem Service',ORG
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,end_char=13436,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=13436""}",,end_char=13436,PERSON
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,PowerShell Empire,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""PowerShell Empire""}",,PowerShell Empire,GPE
+1.0,,Local Area Network,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Local Area Network""}",,Local Area Network,ORG
+1.0,,Windows XP,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Windows XP""}",,Windows XP,LOC
+1.0,,2003,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""2003""}",,2003,DATE
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,PowerShell,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""PowerShell""}",,PowerShell,ORG
+1.0,,Invoke-Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Invoke-Mimikatz""}",,Invoke-Mimikatz,ORG
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,several years,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""several year""}",,several years,DATE
+1.0,,2011,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""2011""}",,2011,DATE
+1.0,,DigiNotar,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""DigiNotar""}",,DigiNotar,ORG
+1.0,,a month,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""a month""}",,a month,DATE
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,NotPetya,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""NotPetya""}",,NotPetya,ORG
+1.0,,BadRabbit,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""BadRabbit""}",,BadRabbit,ORG
+1.0,,2017,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""2017""}",,2017,DATE
+1.0,,Microsoft,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Microsoft""}",,Microsoft,ORG
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,Microsoft,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Microsoft""}",,Microsoft,ORG
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,Windows 8.1/Server,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Windows 8.1/Server""}",,Windows 8.1/Server,ORG
+1.0,,"href=""https://support.microsoft.com","{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""href=\""https://support.microsoft.com""}",,"href=""https://support.microsoft.com",ORG
+1.0,,2014-93434251-04ac,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""2014-93434251-04ac""}",,2014-93434251-04ac,DATE
+1.0,,"target=""_blank","{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""target=\""_blank""}",,"target=""_blank",PERSON
+1.0,,May 13,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""May 13""}",,May 13,DATE
+1.0,,Credential Guard.</p>\n,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Credential Guard.</p>\\n""}",,Credential Guard.</p>\n,ORG
+1.0,,>Credential Guard,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": "">Credential Guard""}",,>Credential Guard,ORG
+1.0,,Microsoft,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Microsoft""}",,Microsoft,ORG
+1.0,,Windows Hardware Compatibility Program Specifications,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Windows Hardware Compatibility Program Specifications""}",,Windows Hardware Compatibility Program Specifications,ORG
+1.0,,2016,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""2016""}",,2016,DATE
+1.0,,Windows Server Semi-Annual,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Windows Server Semi-annual""}",,Windows Server Semi-Annual,LOC
+1.0,,li,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""li""}",,li,PERSON
+1.0,,Microsoft,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Microsoft""}",,Microsoft,ORG
+1.0,,"href=""https://learn.microsoft.com","{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""href=\""https://learn.microsoft.com""}",,"href=""https://learn.microsoft.com",ORG
+1.0,,Windows 10,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Windows 10""}",,Windows 10,LOC
+1.0,,Windows,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Windows""}",,Windows,LOC
+1.0,,4769,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""4769""}",,4769,DATE
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,end_char=14141,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=14141""}",,end_char=14141,ORG
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,end_char=14305,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=14305""}",,end_char=14305,PERSON
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,Entity(text='PowerShell Empire',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Entity(text='PowerShell Empire'""}",,Entity(text='PowerShell Empire',ORG
+1.0,,Area Network',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Area Network'""}",,Area Network',PERSON
+1.0,,end_char=14911,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=14911""}",,end_char=14911,ORG
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,XP,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""XP""}",,XP,ORG
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,2003,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""2003""}",,2003,DATE
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,Entity(text='PowerShell,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Entity(text='PowerShell""}",,Entity(text='PowerShell,ORG
+1.0,,PowerShell,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""PowerShell""}",,PowerShell,ORG
+1.0,,Invoke-Mimikatz',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Invoke-Mimikatz'""}",,Invoke-Mimikatz',ORG
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,several year',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""several year'""}",,several year',DATE
+1.0,,end_char=15614,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=15614""}",,end_char=15614,PERSON
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,end_char=15736,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=15736""}",,end_char=15736,PERSON
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,Entity(text='NotPetya,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Entity(text='NotPetya""}",,Entity(text='NotPetya,PERSON
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,NotPetya,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""NotPetya""}",,NotPetya,ORG
+1.0,,end_char=15962,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=15962""}",,end_char=15962,PERSON
+1.0,,BadRabbit,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""BadRabbit""}",,BadRabbit,ORG
+1.0,,2017,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""2017""}",,2017,DATE
+1.0,,Microsoft,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Microsoft""}",,Microsoft,ORG
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,"end_char=16742, confidence=1.0, metadata={'extraction_method","{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=16742, confidence=1.0, metadata={'extraction_method""}",,"end_char=16742, confidence=1.0, metadata={'extraction_method",ORG
+1.0,,Microsoft,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Microsoft""}",,Microsoft,ORG
+1.0,,Mimikatz,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Mimikatz""}",,Mimikatz,PERSON
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,"href=""https://support.microsoft.com","{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""href=\""https://support.microsoft.com""}",,"href=""https://support.microsoft.com",ORG
+1.0,,2014-93434251-04ac',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""2014-93434251-04ac'""}",,2014-93434251-04ac',DATE
+1.0,,May 13',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""May 13'""}",,May 13',DATE
+1.0,,label='LAW,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""label='law""}",,label='LAW,PERSON
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,Credential Guard.</p>\n,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""credential guard.</p>\\n""}",,Credential Guard.</p>\n,ORG
+1.0,,end_char=17635,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=17635""}",,end_char=17635,PERSON
+1.0,,Microsoft,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Microsoft""}",,Microsoft,ORG
+1.0,,Hardware Compatibility Program Specifications',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Hardware Compatibility Program Specifications'""}",,Hardware Compatibility Program Specifications',ORG
+1.0,,Windows Hardware Compatibility Program Specifications',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Windows Hardware Compatibility Program Specifications'""}",,Windows Hardware Compatibility Program Specifications',ORG
+1.0,,Entity(text='2016',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Entity(text='2016'""}",,Entity(text='2016',ORG
+1.0,,2016,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""2016""}",,2016,DATE
+1.0,,Server Semi-Annual',"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Server Semi-annual'""}",,Server Semi-Annual',PERSON
+1.0,,end_char=17759,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=17759""}",,end_char=17759,ORG
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,end_char=17817,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=17817""}",,end_char=17817,ORG
+1.0,,end_char=17919,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=17919""}",,end_char=17919,ORG
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,Microsoft,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Microsoft""}",,Microsoft,ORG
+1.0,,"href=""https://learn.microsoft.com","{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""href=\""https://learn.microsoft.com""}",,"href=""https://learn.microsoft.com",PERSON
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,end_char=18149,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""end_char=18149""}",,end_char=18149,ORG
+1.0,,confidence=1.0,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""confidence=1.0""}",,confidence=1.0,ORG
+1.0,,Local Administrator Password Solution,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Local Administrator Password Solution""}",,Local Administrator Password Solution,ORG
+1.0,,Microsoft,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Microsoft""}",,Microsoft,ORG
+1.0,,Windows,"{""extraction_method"": ""ml"", ""model"": ""en_core_web_sm"", ""lemma"": ""Windows""}",,Windows,LOC
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diff --git a/cookbook/use_cases/intelligence/criminal_network_relationships.csv b/cookbook/use_cases/intelligence/criminal_network_relationships.csv
new file mode 100644
index 00000000..49bcea37
--- /dev/null
+++ b/cookbook/use_cases/intelligence/criminal_network_relationships.csv
@@ -0,0 +1,177 @@
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