Don't trust model quotes; use anchors instead

I asked models to extract facts with provenance two different ways. Naive: return {answer, quote}, insist the quote is verbatim, then string-match it against the source. Exact-match rates ran 37–93% depending on model quality. Fabrication was real — 0–13%. Normalization helped only slightly. Every tier could produce prose that reads like a quote and isn't. Anchor: return {answer, anchor_phrase} — three to eight words near the value. Feed the anchor to deterministic code that searches the source: exact match, then unicode/whitespace normalization, then ordered-token subsequence, then fuzzy match. If located, emit the containing sentence from the source itself. If not, return not_found and fail closed. Anchor coverage: 93–100%. Every span the code emits is a real substring by construction. Provenance fidelity for anything located is 100%. The chaotic tier quoted exact 37% of the time and still recovered 93% anchor coverage. That is the whole argument in one row. T