diff --git a/cookbook/use_cases/finance/03_Earnings_Call_Analysis.ipynb b/cookbook/use_cases/finance/03_Earnings_Call_Analysis.ipynb
index 715d221e..2c629da5 100644
--- a/cookbook/use_cases/finance/03_Earnings_Call_Analysis.ipynb
+++ b/cookbook/use_cases/finance/03_Earnings_Call_Analysis.ipynb
@@ -35,14 +35,14 @@
     "## End-to-End Workflow\n",
     "\n",
     "**Workflow:**  \n",
-    "Dual PDF Input → Docling Parsing → Normalization & Chunking → Entity, Relation & Triplet Extraction → Conflict Resolution & Deduplication → Knowledge Graph Construction → Amazon Neptune → GraphRAG → Agent Memory & Context → Strategic Q&A\n",
+    "Dual PDF Input → Docling Parsing → Normalization & Chunking → Entity, Relation Extraction → Conflict Resolution & Deduplication → Knowledge Graph Construction → Amazon Neptune → GraphRAG → Agent Memory & Context → Strategic Q&A\n",
     "\n",
     "---\n",
     "\n",
     "## Pipeline Capabilities\n",
     "\n",
     "- High-fidelity PDF parsing (text, tables, structure)  \n",
-    "- Semantic extraction of entities, relationships, and triplets  \n",
+    "- Semantic extraction of entities, and relationships\n",
     "- Conflict detection and resolution with confidence awareness  \n",
     "- Entity deduplication and canonicalization  \n",
     "- Knowledge graph construction and validation  \n",
@@ -280,7 +280,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
     "from semantica.semantic_extract import NERExtractor\n",
     "\n",
     "ner = NERExtractor(\n",
@@ -291,25 +290,19 @@
     "    api_key=GROQ_API_KEY,\n",
     ")\n",
     "\n",
-    "ENTITY_TYPES = [\n",
-    "    \"ORGANIZATION\", \"ORG\", \"PERSON\", \"MONEY\", \"CURRENCY\",\n",
-    "    \"PERCENT\", \"PERCENTAGE\", \"DATE\", \"TIME\", \"PRODUCT\",\n",
-    "    \"LOCATION\", \"GPE\", \"EVENT\", \"QUANTITY\", \"CARDINAL\",\n",
+    "ENTITY_TYPES = [\"ORGANIZATION\", \"PERSON\", \"MONEY\", \"PERCENT\", \"DATE\", \"EVENT\"]\n",
+    "\n",
+    "all_entities = [\n",
+    "    e\n",
+    "    for c in chunks\n",
+    "    for e in ner.extract_entities(\n",
+    "        get_chunk_text(c),\n",
+    "        entity_types=ENTITY_TYPES,\n",
+    "    )\n",
+    "    if get_chunk_text(c).strip()\n",
     "]\n",
     "\n",
-    "all_entities = []\n",
-    "\n",
-    "for chunk in chunks:\n",
-    "    text = get_chunk_text(chunk)\n",
-    "    if text.strip():\n",
-    "        all_entities += ner.extract_entities(text, entity_types=ENTITY_TYPES)\n",
-    "\n",
-    "print(\"Entity extraction completed\")\n",
-    "print(\"Total entities extracted:\", len(all_entities))\n",
-    "\n",
-    "print(\"\\nSample entities\")\n",
-    "for e in all_entities[:10]:\n",
-    "    print(f\"{e.label}: {e.text}\")"
+    "print(\"Entities:\", len(all_entities))"
    ]
   },
   {
@@ -382,105 +375,39 @@
     "\n",
     "relation_extractor = RelationExtractor(\n",
     "    method=\"llm\",\n",
-    "    confidence_threshold=0.5,\n",
+    "    confidence_threshold=0.6,\n",
     "    relation_types=[\n",
-    "        \"HAS_REVENUE\", \"HAS_EPS\", \"HAS_MARGIN\", \"HAS_PROFIT\", \"HAS_GROWTH\",\n",
-    "        \"PROVIDES_GUIDANCE\", \"STATES\", \"ANNOUNCES\", \"REPORTS\", \"EXPECTS\",\n",
-    "        \"OPERATES_IN\", \"LOCATED_IN\", \"PARTNERS_WITH\", \"SERVES\",\n",
-    "        \"COMPARED_TO\", \"INCREASED_BY\", \"DECREASED_BY\", \"CHANGED_BY\",\n",
-    "        \"DURING\", \"IN_QUARTER\", \"FOR_PERIOD\",\n",
-    "        \"RELATED_TO\", \"PART_OF\", \"AFFECTS\",\n",
+    "        \"HAS_REVENUE\",\n",
+    "        \"HAS_GROWTH\",\n",
+    "        \"REPORTS\",\n",
+    "        \"PROVIDES_GUIDANCE\",\n",
+    "        \"IN_QUARTER\",\n",
+    "        \"FOR_PERIOD\",\n",
+    "        \"RELATED_TO\",\n",
     "    ],\n",
     "    api_key=GROQ_API_KEY,\n",
     ")\n",
     "\n",
-    "def get_chunk_text(chunk):\n",
-    "    return getattr(chunk, \"content\", getattr(chunk, \"text\", \"\")) or \"\"\n",
-    "\n",
-    "relationships = []\n",
-    "\n",
-    "for chunk in chunks:\n",
-    "    text = get_chunk_text(chunk)\n",
-    "\n",
-    "    relations = relation_extractor.extract_relations(\n",
-    "        text,\n",
+    "relationships = [\n",
+    "    r\n",
+    "    for c in chunks\n",
+    "    for r in relation_extractor.extract_relations(\n",
+    "        text=get_chunk_text(c),\n",
     "        entities=all_entities,\n",
     "        provider=\"groq\",\n",
     "        llm_model=\"llama-3.1-8b-instant\",\n",
     "        temperature=0.0,\n",
     "    )\n",
-    "\n",
-    "    relationships += relations\n",
-    "\n",
-    "print(\"Relationship extraction completed\")\n",
-    "print(\"Total chunks:\", len(chunks))\n",
-    "print(\"Total relationships extracted:\", len(relationships))\n",
-    "\n",
-    "if relationships:\n",
-    "    r = relationships[0]\n",
-    "    print(\"Sample relationship:\")\n",
-    "    print(f\"{r.subject.text} → {r.predicate} → {r.object.text}\")"
+    "    if get_chunk_text(c).strip()\n",
+    "]\n",
+    "print(\"Relationships:\", len(relationships))\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 6: Extract RDF Triplets\n",
-    "\n",
-    "Extract RDF triplets (subject-predicate-object) using TripletExtractor with Groq LLM.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from semantica.semantic_extract import TripletExtractor\n",
-    "\n",
-    "triplet_extractor = TripletExtractor(\n",
-    "    method=\"llm\",\n",
-    "    include_temporal=True,\n",
-    "    include_provenance=True,\n",
-    "    provider=\"groq\",\n",
-    "    llm_model=\"llama-3.1-8b-instant\",\n",
-    "    temperature=0.0,\n",
-    "    api_key=GROQ_API_KEY,\n",
-    ")\n",
-    "\n",
-    "def get_chunk_text(chunk):\n",
-    "    return getattr(chunk, \"content\", getattr(chunk, \"text\", \"\")) or \"\"\n",
-    "\n",
-    "triplets = []\n",
-    "\n",
-    "for chunk in chunks:\n",
-    "    text = get_chunk_text(chunk)\n",
-    "\n",
-    "    triplets += triplet_extractor.extract_triplets(\n",
-    "        text,\n",
-    "        entities=all_entities,\n",
-    "        relations=relationships if relationships else None,\n",
-    "    )\n",
-    "\n",
-    "if hasattr(triplet_extractor, \"validate_triplets\"):\n",
-    "    triplets = triplet_extractor.validate_triplets(triplets)\n",
-    "\n",
-    "print(\"Triplet extraction completed\")\n",
-    "print(\"Total chunks:\", len(chunks))\n",
-    "print(\"Total RDF triplets:\", len(triplets))\n",
-    "\n",
-    "if triplets:\n",
-    "    t = triplets[0]\n",
-    "    print(\"Sample triplet:\")\n",
-    "    print(f\"{t.subject} → {t.predicate} → {t.object}\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 7: Detect Conflicts\n",
+    "## Step 6: Detect Conflicts\n",
     "\n",
     "Detect conflicts in extracted entities and relationships using ConflictDetector.\n"
    ]
@@ -533,7 +460,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 8: Resolve Conflicts\n",
+    "## Step 7: Resolve Conflicts\n",
     "\n",
     "Resolve detected conflicts using ConflictResolver with voting strategy.\n"
    ]
@@ -571,7 +498,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 9: Deduplicate Entities\n",
+    "## Step 8: Deduplicate Entities\n",
     "\n",
     "Detect and merge duplicate entities using DuplicateDetector and EntityMerger.\n"
    ]
@@ -621,7 +548,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 10: Build Knowledge Graph\n",
+    "## Step 9: Build Knowledge Graph\n",
     "\n",
     "Build knowledge graph from cleaned entities, relationships, and triplets using GraphBuilder.\n"
    ]
@@ -676,7 +603,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 11: Analyze Knowledge Graph\n",
+    "## Step 10: Analyze Knowledge Graph\n",
     "\n",
     "This step evaluates the structure and quality of the knowledge graph.\n",
     "\n",
@@ -726,7 +653,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 12: Persist Knowledge Graph in Amazon Neptune\n",
+    "## Step 11: Persist Knowledge Graph in Amazon Neptune\n",
     "\n",
     "After cleaning, conflict resolution, and deduplication, the final step is to\n",
     "persist the **canonical knowledge graph** into a production graph database.\n",
@@ -841,7 +768,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 13: Context Retrieval\n",
+    "## Step 12: Context Retrieval\n",
     "\n",
     "Set up hybrid retrieval (vector + graph) using ContextRetriever for GraphRAG queries.\n"
    ]
@@ -894,7 +821,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 14: Agent Memory (Long-Term Context)\n",
+    "## Step 13: Agent Memory (Long-Term Context)\n",
     "\n",
     "This step enables long-term memory for agents by storing important facts,\n",
     "metrics, and entities extracted from the knowledge graph.\n",
@@ -965,7 +892,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 15: Agent Context\n",
+    "## Step 14: Agent Context\n",
     "\n",
     "**AgentContext** provides a unified context layer that combines **vector-based RAG**\n",
     "with **graph-based GraphRAG** for grounded and explainable retrieval.\n",
@@ -1063,7 +990,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 16: Answer Generation\n",
+    "## Step 15: Answer Generation\n",
     "\n",
     "Generate answers to financial questions using Groq LLM with retrieved context and knowledge graph.\n"
    ]
@@ -1133,7 +1060,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 17: Export Results\n",
+    "## Step 16: Export Results\n",
     "\n",
     "Export knowledge graph and analysis results to JSON and RDF formats.\n"
    ]
@@ -1155,7 +1082,6 @@
     "analysis_summary = {\n",
     "    \"entities\": len(knowledge_graph.get(\"entities\", [])),\n",
     "    \"relationships\": len(knowledge_graph.get(\"relationships\", [])),\n",
-    "    \"triplets\": len(triplets),\n",
     "    \"conflicts_resolved\": len(resolved_conflicts),\n",
     "    \"merged_entities\": len(merged_entities),\n",
     "    \"communities\": num_communities,\n",