Knowledge graphs need more than LLM extraction
Large have contracts, rules, technical documents, and manuals with sections, definitions, and cross-references, but those documents are not easy for software to use reliably. An LLM can pull out entities, relationships, and rules from this kind of text, but that is only the first step. The same document can produce different results if the model, prompt, or surrounding text changes.
The ontology also changes over time when new document types appear, experts correct the design, or other teams need different data shapes. Extracted data should be checked before storage with , duplicate checks, conflict checks, and basic sanity checks on values. Every item in the graph should keep provenance, including the source document, , extraction time, validation result, and status.
High-confidence results can move forward automatically, while unclear or conflicting results should go to a queue. A verified evaluation dataset is needed so teams can test quality whenever prompts, models, or schemas change.
Key points
- LLM extraction is useful, but it is not enough to build a dependable .
- Extracted data should pass , duplicate checks, conflict checks, and value checks before storage.
- Each node and relationship needs provenance so teams can trace it back to the source and .
- The ontology will change, so schema changes should be versioned and migrated carefully.
- Human corrections should feed an evaluation dataset used to test prompt, model, and schema changes.