Extract-time provenance (#661)

1. Shared Provenance Module - URI generators, namespace constants,
   triple builders, vocabulary bootstrap
2. Librarian - Emits document metadata to graph on processing
   initiation (vocabulary bootstrap + PROV-O triples)
3. PDF Extractor - Saves pages as child documents, emits parent-child
   provenance edges, forwards page IDs
4. Chunker - Saves chunks as child documents, emits provenance edges,
   forwards chunk ID + content
5. Knowledge Extractors (both definitions and relationships):
   - Link entities to chunks via SUBJECT_OF (not top-level document)
   - Removed duplicate metadata emission (now handled by librarian)
   - Get chunk_doc_id and chunk_uri from incoming Chunk message
6. Embedding Provenance:
   - EntityContext schema has chunk_id field
   - EntityEmbeddings schema has chunk_id field
   - Definitions extractor sets chunk_id when creating EntityContext
   - Graph embeddings processor passes chunk_id through to
     EntityEmbeddings

Provenance Flow:
Document → Page (PDF) → Chunk → Extracted Facts/Embeddings
    ↓           ↓          ↓              ↓
  librarian  librarian  librarian    (chunk_id reference)
  + graph    + graph    + graph

Each artifact is stored in librarian with parent-child linking, and PROV-O
edges are emitted to the knowledge graph for full traceability from any
extracted fact back to its source document.

Also, updating tests

trustgraph-base/trustgraph/schema/knowledge/document.py

@@ -34,5 +34,9 @@ class TextDocument:
 class Chunk:
     metadata: Metadata | None = None
     chunk: bytes = b""
+    # For provenance: document_id of this chunk in librarian
+    # Post-chunker optimization: both document_id AND chunk content are included
+    # so downstream processors have the ID for provenance and content to work with
+    document_id: str = ""
 
 ############################################################################

trustgraph-base/trustgraph/schema/knowledge/embeddings.py

@@ -12,6 +12,8 @@ from ..core.topic import topic
 class EntityEmbeddings:
     entity: Term | None = None
     vectors: list[list[float]] = field(default_factory=list)
+    # Provenance: which chunk this embedding was derived from
+    chunk_id: str = ""
 
 # This is a 'batching' mechanism for the above data
 @dataclass

trustgraph-base/trustgraph/schema/knowledge/graph.py

@@ -12,6 +12,8 @@ from ..core.topic import topic
 class EntityContext:
     entity: Term | None = None
     context: str = ""
+    # Provenance: which chunk this entity context was derived from
+    chunk_id: str = ""
 
 # This is a 'batching' mechanism for the above data
 @dataclass

trustgraph-base/trustgraph/schema/services/library.py

@@ -91,7 +91,12 @@ class DocumentMetadata:
     tags: list[str] = field(default_factory=list)
     # Child document support
     parent_id: str = ""  # Empty for top-level docs, set for children
-    document_type: str = "source"  # "source" or "extracted"
+    # Document type vocabulary:
+    #   "source" - original uploaded document
+    #   "page" - page extracted from source (e.g., PDF page)
+    #   "chunk" - text chunk derived from page or source
+    #   "extracted" - legacy value, kept for backwards compatibility
+    document_type: str = "source"
 
 @dataclass
 class ProcessingMetadata: