feat(context): add to_kg_dict() adapter for canonical KG shape (#1081)

* feat(context): add to_kg_dict() adapter for canonical KG shape

Convert ContextGraph internal nodes/edges/source representation into the canonical entities/relationships/source_id shape consumed by RDFExporter and TemporalGraphQuery. Add entities_only filtering that drops dangling relationships, plus README examples and unit tests.

* fix(context): harden to_kg_dict against null props and non-str node ids

- Guard properties/metadata with 'or {}' so nodes loaded from JSON null
  no longer raise TypeError when copied (Qodo bug 1)
- Coerce entity id to str(n.node_id) so it matches ContextEdge's
  str-coerced endpoints, preventing valid relationships from being
  dropped during entities_only filtering (Qodo bug 3)

* fix(kg): accept source_id/target_id endpoints in validator and temporal query

to_kg_dict() emits canonical source_id/target_id keys, but GraphValidator
and TemporalGraphQuery only read the legacy source/target keys, so its
output failed validation and lost relationships (Qodo bug 2).

- GraphValidator: resolve endpoints from either key variant and treat a
  resolvable source/target (plus type) as satisfying required fields
- TemporalGraphQuery.analyze_evolution/find_paths: read either variant
- tests: add regression coverage for null props/metadata (bug 1),
  non-string node ids (bug 3), and KG-utility consumability (bug 2)

---------

Co-authored-by: 江俊杰 <jiangjunjie.37@jd.com>

README.md

@@ -303,17 +303,10 @@ graph.add_causal_relationship(d1, d2, relationship_type="CAUSED")
 prov.track_entity("patient_P4821", source="ehr/medication_orders_2024.json",
                   metadata={"extractor": "NamedEntityRecognizer"})
 
-# Export W3C PROV-O for regulator submission - RDFExporter expects
-# {"entities": [...], "relationships": [...]}, so map ContextGraph.to_dict()'s
-# {"nodes": [...], "edges": [...]} shape onto it first
-graph_dict = graph.to_dict()
-kg = {
-    "entities": [{"id": n["id"], "type": n["type"], "text": n["content"]} for n in graph_dict["nodes"]],
-    "relationships": [
-        {"source_id": e["source"], "target_id": e["target"], "type": e["type"]}
-        for e in graph_dict["edges"]
-    ],
-}
+# Export W3C PROV-O for regulator submission - to_kg_dict() is the official
+# adapter that emits the {"entities": [...], "relationships": [...]} /
+# source_id shape RDFExporter expects, so no manual field mapping is needed
+kg = graph.to_kg_dict()
 RDFExporter().export(kg, "audit_trail.ttl", format="turtle")
 ```
 
@@ -887,20 +880,14 @@ fact = BiTemporalFact(
     recorded_at=datetime(2024, 3, 5),
 )
 
-# Query facts valid within a time window - query_time_range() expects
-# {"relationships": [...]} with source_id/target_id keys, which differs from
-# ContextGraph.to_dict()'s {"nodes", "edges"} shape, so map it first
-graph_dict = graph.to_dict()
-kg_relationships = {
-    "relationships": [
-        {**e, "source_id": e["source"], "target_id": e["target"]}
-        for e in graph_dict["edges"]
-    ]
-}
+# Query facts valid within a time window - to_kg_dict() is the official
+# adapter that emits {"entities", "relationships"} with source_id/target_id
+# keys, the shape query_time_range() expects (no manual mapping required)
+kg = graph.to_kg_dict()
 
 tq = TemporalGraphQuery()
 facts_in_window = tq.query_time_range(
-    kg_relationships, query="valid_facts", start_time="2024-01-01", end_time="2024-12-31"
+    kg, query="valid_facts", start_time="2024-01-01", end_time="2024-12-31"
 )
 
 # Normalize natural language temporal expressions - returns a (start, end) range