The extractor classified rule_type by SOURCE bindingness (higher-court→binding,
committee→persuasive) instead of by rule KIND. The gold-set proved it: 'binding'
appeared on 19/19 external rulings & 0 committees; 'persuasive' on 13/13
committees & 0 external — only 58% agreement with the human role tags. The two
axes (authority vs rule role) were crammed into one enum.
This splits them per INV-DM7:
- authority (binding/persuasive) — DERIVED from case_law.precedent_level
(עליון/מנהלי→binding, ועדת_ערר_מחוזית→persuasive), never stored, never
LLM-guessed. New helper halacha_quality.derive_authority; surfaced read-only
in list_halachot / goldset_list / search results.
- rule_type — now the rule ROLE only: holding/interpretive/procedural/
application/obiter. Both extractor prompts unified to this vocabulary;
_coerce_halacha no longer defaults rule_type from the source; legacy
binding→holding / persuasive→interpretive fold for safety.
UI: authority shown as a separate read-only badge (gold=מחייב / muted=משכנע)
across the review queue, precedent detail, and gold-set; the gold-set role
selector drops binding/persuasive and adds מהותי (holding).
Migration: scripts/halacha_rule_role_backfill.py re-classifies the 276 pre-split
binding/persuasive rows into a genuine role via local claude_session (run after
deploy). Gold-set correct_type/ai_correct_type 'binding'→'holding' via SQL.
Sources (≥3, per research-decision policy): OASIS LegalRuleML v1.0
(appliesAuthority/Strength as metadata orthogonal to rule logic) · SemEval-2023
Task 6 LegalEval (rhetorical roles by function, authority kept separate) ·
Bluebook signals (weight-of-authority is a separate dimension).
Invariants: ESTABLISHES INV-DM7. Upholds G1 (normalize at source — extractor
classifies role, system derives authority) and G2 (single source of truth —
authority derived, not a parallel stored field). Tests: 211 pass + new
derive_authority/coerce coverage. web-ui build + tsc clean.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Halacha-extraction quality (#81) and dedup-on-insert (#82) — engine changes
(pure + tested) plus measurement/ops tooling.
halacha_quality.py
- #81.4 application gate: is_fact_dependent() (high-precision "applied to THIS
case" deixis per the strict rubric §3/§27) + FLAG_APPLICATION. compute_quality_flags
now takes rule_type and flags rule_type=='application' OR fact-dependent —
blocking auto-approve (an illustration is not a generalizable holding).
- #82.3 lexical tail signal: jaccard_shingles / normalized_levenshtein /
lexical_near_duplicate + FLAG_NEAR_DUPLICATE, for the 0.83–0.93 cosine band.
halacha_extractor.py — pass rule_type to the flag computation; re-type a
binding-labeled fact-application to 'application' (mirrors non_decision→obiter).
db.py (store_halachot_for_chunk) — dedup now fetches the nearest same-precedent
neighbor once: cosine ≥ DEDUP → skip (unchanged); cosine in [BAND, DEDUP) with
high lexical overlap → FLAG_NEAR_DUPLICATE (review, not skip — never drop a
possibly-distinct principle unreviewed).
config.py — HALACHA_DEDUP_BAND_COSINE (0.83).
Scripts:
- scripts/halacha_goldset.py (#81.7) — export stratified sample for human
tagging; score validators (P/R/F1) against the tags. Backbone for #81.8.
- scripts/halacha_batch_reconcile.py (#82.7) — conservative cross-precedent
dedup (cosine ≥0.95), dry-run report only.
- scripts/calibrate_halacha_dedup.py (#82.1) — calibrate the lexical thresholds
against the 2026-06-03 cleanup gold-set.
Deferred (documented): #82.4 merge-provenance and #82.5 DB ON CONFLICT/UNIQUE
on normalized quote are NOT included — the current skip+flag behavior is safe,
whereas a UNIQUE on normalized_quote would fail on existing dups and a blind
merge risks losing provenance; they need their own chair-reviewed migration.
#82.6 over-merge guard is moot until merge lands. #81.6 full rhetorical-role
classifier deferred (section pre-filter + application flag cover the practical
case); #81.8 blocked on the human-tagged gold-set (harness now provided).
Verified:
- pytest tests/test_halacha_quality.py — 52 passed (14 new).
- calibrate: configured (0.55,0.70) → precision 1.0 (zero false-merge), recall
0.30 — correct profile for an auto-approve-blocking signal.
- goldset export: 15-row sample CSV. batch reconcile: 819 halachot → 5
cross-precedent candidate pairs.
Invariants: G1 (normalize at source — flag at insert, not at read); §6 (no
silent swallow — suspect items flagged to review, never dropped); G2 (no
parallel path — same store_halachot_for_chunk / compute_quality_flags).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
After a precedent finishes extracting, a claude_session pass folds facets of the
SAME legal question (below #82's dedup cosine — the שפר 14-vs-4 / 403-17→89
granularity gap) into one canonical; the rest are marked 'rejected' (reversible:
out of the active corpus AND the review queue, but recoverable). FOLD-ONLY —
never merges distinct legal questions, never invents.
- Engine: claude_session-as-judge (local CLI, zero cost), 'high' effort — folding
needs careful judgment. One pass per precedent, runs in _extract_impl once all
chunks are done (the prompt dedups within a chunk; this catches across chunks).
- Pure, unit-tested helpers in halacha_quality: CONSOLIDATE_SYSTEM,
build_consolidation_prompt, parse_fold_groups (fails SAFE → [] on any malformed
shape; drops <2-member groups; coerces/dedups indices).
- halacha_extractor._consolidate_precedent picks the canonical per group
(approved>pending, higher confidence, quote_verified, longer) and rejects the
rest via the existing update_halachot_batch (#84). Never rejects a canonical.
Fails OPEN on any error (no CLI / parse fail → 0 folds, data untouched).
- config: HALACHA_CONSOLIDATE_ENABLED/MODEL/EFFORT.
Verified: suite 176 passed (10 new); integration vs dev DB — a 2-facet group
folds to 1 canonical + 1 rejected (tagged), distinct rules untouched, claude
error → 0 folds (fail-open).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
#81.3 — a post-extraction validator that flags halachot whose rule_statement is
NOT entailed by its supporting_quote (the model over-reaching beyond its source).
- Engine: claude_session-as-judge (local CLI, zero API cost) per chaim's standing
preference — one batched judge call per chunk, NOT a hosted NLI model.
- Pure, unit-tested helpers in halacha_quality: NLI_SYSTEM, build_nli_prompt,
parse_nli_verdicts (fails OPEN — any shape/label ambiguity → 'entailed').
- halacha_extractor._nli_check wraps the call; fails OPEN on any error (e.g. no
CLI in the container) so a flaky judge never blocks a genuine halacha.
- Non-entailed (neutral/contradiction) → quality_flag 'nli_unsupported' which
blocks auto-approve (routes to pending_review) via the existing store gate.
- config: HALACHA_NLI_ENABLED/MODEL/EFFORT (effort 'low' — entailment is simple).
Verified: suite 166 passed (10 new); LIVE smoke test against the real claude CLI
returned ['entailed','neutral'] for a supported vs unsupported rule.
Also commits TaskMaster #86 (Nevo preamble/ratio: anti-contamination strip fix +
gold-set benchmark) capturing today's strip_nevo_preamble findings.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Bake the 2026-06-03 strict-cleanup rubric into the extraction pipeline so the
corpus stays clean at the source instead of accumulating duplicates, obiter
dicta, truncated quotes and thin restatements that clog the review queue.
#81 — quality gate:
- New pure module halacha_quality.py with unit-tested validators:
non-decision/obiter (Wambaugh markers), truncated-quote (mid-word cut),
thin-restatement (rule≈quote), quote-unverified.
- Validators run in halacha_extractor._process; a non-decision is re-typed
obiter; flags persist in new halachot.quality_flags column.
- Auto-approve now requires confidence>=threshold AND no quality flags;
flagged items route to pending_review regardless of confidence.
- Both extraction prompts hardened: reject undecided dicta, exclude
case-specific applications, require abstraction, forbid over-splitting.
#82 — dedup-on-insert (store_halachot_for_chunk):
- Within the same precedent, skip a halacha whose normalized supporting_quote
already exists, or whose rule-embedding has cosine>=HALACHA_DEDUP_COSINE
(0.93) against an already-stored one. Makes re-runs idempotent.
Migration: halachot.quality_flags TEXT[] (additive, idempotent ALTER).
Tests: 19 new unit tests; full suite 156 passed. Validated end-to-end against
dev DB (dedup skips dups, flag blocks auto-approve, re-run inserts 0).
Calibration: flags fire on only ~10% of current survivors (low false-positive).
Spec: docs/halacha-strict-rubric.md
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
