Part of #82 (calibrated honesty).
Problem
The over-fit errors ("Quarkus underlies Argo for Cleveron") come from the LLM asserting global claims from a single document's local context. Today there's no record of how many / which docs support a given entity or relationship — so "asserted from 1 doc, uncorroborated" is indistinguishable from "corroborated across 5 docs."
Goal
Track and expose provenance — the source documents supporting each entity and each relationship — as the raw material for confidence (#calibration) and a trust signal in its own right.
Acceptance Criteria
Out of scope
- Using provenance to compute confidence (that's the calibration issue)
- Surfacing it to consumers (that's the retrieval/MCP propagation issue)
Part of #82 (calibrated honesty).
Problem
The over-fit errors ("Quarkus underlies Argo for Cleveron") come from the LLM asserting global claims from a single document's local context. Today there's no record of how many / which docs support a given entity or relationship — so "asserted from 1 doc, uncorroborated" is indistinguishable from "corroborated across 5 docs."
Goal
Track and expose provenance — the source documents supporting each entity and each relationship — as the raw material for confidence (#calibration) and a trust signal in its own right.
Acceptance Criteria
source_documents; ensure it accumulates correctly across the dedup/merge step)uncorroborated/ low-corroboration)Out of scope