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My structured extraction cites a phrase from the document, and the fact it pulled out still is not actually in it.
What you'll have at the end
A graph of entities and relationships where each one carries its source sentence, and a follow-up check flags any that don't hold up.
You need
A working call to an LLM API that already returns subject-predicate-object relationships from a passage using a JSON schema, and one short passage where you know exactly what each sentence does and does not establish.
Not covered
Merging multiple mentions of the same entity into one canonical graph node, and resolving pronouns or aliases across sentences: this recipe grounds one document's relationships against their own citations, not the separate work of unifying entities across a whole corpus.
Leans on
A hallucinated phone number breaks the record
you only need to confirm one flat field is real, not build a graph of cited relationships.
Merge duplicate customer records without a mistake you can't undo
the problem is multiple mentions of the same entity that need merging into one node, not a citation that fails to support its relationship.
Checked 18 Aug 2026
Part of the Structured Output Generation with LLMs cookbook