Near-duplicate (159+ rejections):
- Add extract-time dedup gate: SequenceMatcher check before file write ($0)
- Strengthen extraction prompt: high-similarity matches (>=0.75) get explicit
"DO NOT extract, use enrichment instead" warning
- Strip [[wiki link]] brackets from related_claims field
Frontmatter schema (129+ rejections):
- Normalize LLM confidence aliases (high→likely, medium→experimental, etc.)
in both _build_claim_content and validate_schema
- Strip code fences (```markdown/```yaml) from entity content in extract.py
and from diff content in validate.py tier0.5 check
- Code fences were root cause of "no_frontmatter" failures: parser sees
```markdown as first line, not ---
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Two changes to address the #1 rejection reason:
1. extraction_prompt.py: Explicitly tell LLM NOT to use [[wiki links]]
in body text — use connections/related_claims JSON fields instead.
Remove misleading "post-processor handles wiki links" language.
2. extract.py _get_kb_index(): Expand KB index to include entity stems
from entities/{domain}/ so the LLM knows what entities exist when
building connections. Previously only showed domain claims.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Three fixes for conversation-sourced claim quality:
1. Trust hierarchy in extraction prompt: bot-generated numbers are
flagged as unverified context, not evidence. Directional claims
are extractable but specific figures require external verification.
Prevents laundering bot guesses into the KB as evidence.
2. Conversation-sourced claims tagged with verified: false and
source_type: conversation in frontmatter. Downstream consumers
(Leo, dashboard) can filter/flag these for verification.
3. GET /api/telegram-extractions endpoint for daily spot-checking.
Shows recent Telegram-sourced PRs with claim titles, status,
merge rate, and eval issues. Quick review surface.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Two changes:
1. extract.py: Enrichments now modify existing claim files by appending
evidence sections. Previously enrichment-only extractions were
discarded as null-result even when they contained valuable challenges.
2. extraction_prompt.py: Corrections should produce BOTH a claim (the
corrected knowledge) AND an enrichment (linking to what it corrects).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
When source format is "conversation", inject specialized extraction
rules that prioritize human corrections/pushback as highest-value
content. Fixes null-result on short but high-signal correction
messages. Maps corrections to existing KB claims as challenges.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
