Markdown source

Citation Needed: Provenance for LLM-Built Knowledge Graphs

Conference Context

Session Description

An LLM doesn't copy facts into your knowledge graph. It synthesizes them: entities merge across sources, and later data invalidates earlier facts. By the time your agent retrieves "patient has a penicillin allergy," the origin — an EHR record, a lab report, or something typed into a chatbot — is gone. This talk covers engineering lineage into a lossy, generative pipeline: episode-to-fact links as structural graph properties, provenance that survives entity resolution, metadata projection (tag a source once; it follows every derived node and edge), and the query semantics of filtering facts by ancestry, including mixed-trust parentage. Deletion is the inverse problem: GDPR erasure propagates back through the same derivation edges. Compliance gets an audit trail; engineers get agents they can debug instead of black boxes.

Media Evidence

Stop Using RAG as Memory — Daniel Chalef, Zep (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

Evidence Graph

This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

Media Signals

Agent Reading Notes

Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

Transcript Status

Related video transcript availability: English auto-captions. Treat this as supporting context, not a recording of this exact scheduled session unless later confirmed. Not fetched yet.

People

Supporting Slides

Slide Evidence

Synthesis

Synthesized Breakdown

Citation Needed: Provenance for LLM-Built Knowledge Graphs ## Conference Context - Date/time: 2026-07-01 · 3:20pm-3:40pm - Track/room: Graphs · Track 5 - Speaker(s): Daniel Chalef - Session type/status: sponsor · confirmed - Track: Graphs - Room: Track 5 - Session type: sponsor - Status: confirmed ## Session Description An LLM doesn't copy facts into your knowledge graph. It synthesizes them: entities merge across sources, and later data invalidates earlier facts. By the time your agent retrieves "patient has a penicillin allergy," the origin — an EHR record, a lab report, or something typed into a chatbot — is gone. This talk covers engineering lineage into a lossy, generative pipeline: episode-to-fact links as structural graph properties, provenance that survives entity resolution, metadata projection (tag a source once; it follows every derived node and edge), and the query semantics of filtering facts by ancestry, including mixed-trust parentage.

Speaker And Company Context

Topics Covered

Derived Links And Source Material

Novel Concepts / Clever Methods

Evidence Boundary

This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.