core/docs/decisions/ADR-0194-labeled-container-subject.md

ADR-0194 — Labeled-container subject entity shape

Status: Proposed (implemented in this PR). Extends: ADR-0136.S.4 (sibling-pattern localisation), ADR-0123a (entity slot). Composes with: ADR-0193 (aggregate question frame). Substrate: 0 real-corpus metric flip by design; the value is the entity-shape generalisation + proven composition with the aggregate question.

One line. GSM8K labels containers/regions with a trailing single-letter or short-numeric label ("Jar A has 28 marbles", "Section G has 10 cars", "District 2 has 19 voters"). The initial-possession entity slot (_ENTITY = (?:[A-Z]\w+|[Tt]he\s+\w+)) captures only "Jar" and then expects the possession verb, so the label breaks the match and the statement parses to nothing. This adds a separate sibling pattern that REQUIRES a label.

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1. The gap

Both reader paths reject the labeled subject:

• the candidate parser's _INITIAL_HAS_RE (extract_initial_candidates → 0 candidates);
• the recognizer's discrete_count anchor (proper-noun single-token subject → 0 anchors).

"Jamie has 28 marbles" parses (1 candidate); "Jar A has 28 marbles" does not — purely because of the trailing label.

2. Decision

Add _INITIAL_HAS_LABELED_RE in generate/math_candidate_parser.py, consumed by a dedicated _init_has_labeled_candidates helper wired into extract_initial_candidates:

^<Noun> <label> (has|have|had|started) <value> [adj] [unit] [of/in/for/with …]
   label = a single uppercase letter OR 1-2 digits

• Sibling-pattern localisation (mirrors ADR-0136.S.4's _INITIAL_HAS_INDEF_RE): the global _ENTITY is unchanged for every other path (operations, comparisons, questions).
• The label is required, so a bare subject ("Jamie has 28 marbles") never reaches this pattern and yields no duplicate candidate.
• The label is bounded by the following possession verb, so a multi-word noun does NOT match: "Jar Apple has 5 marbles" → no candidate ("Apple" is not a single-letter label), "Box Set has 12 items" → no candidate.
• Same value/unit tail and money normalisation as _INITIAL_HAS_RE.

Why this is safe (the firewall is the precondition)

The label widening only makes a statement parse into an initial possession. wrong=0 is held downstream by the completeness guard (ADR-0191) + the round-trip filter + branch disagreement — a mis-parse leaves source quantities uncovered and refuses. Full-corpus verification: wrong=0 HOLDS (7,473 q); train_sample byte-identical 4/46/0; the synthetic-registry capability-axis wrong=0 gate and the G5 aggregate lane both stay green.

3. Evidence

• Composes with ADR-0193: "Jar A has 28 marbles. Jar B has 12 marbles. How many marbles are there in total?" → 40.0; three-container variant → 10.0.
• 0 real-corpus flip (honest framing): of the 3 real container-subject problems under an aggregate question, the only multi-container aggregate ("Jar A has 28 marbles. Jar B has 12 more marbles than jar A. Jar C has twice as many as jar B. … altogether?") additionally requires **comparative-additive
  • comparative-multiplicative + lowercase-reference** resolution. This is the composition-wall lesson again: an entity-shape widening is necessary, not sufficient.
• Tests: tests/test_labeled_container_subject.py (labeled containers parse; bare subjects yield no duplicate; multi-word nouns don't match a label; composes with the aggregate question).

4. Consequences & follow-ups

• The labeled-container entity shape is banked; it becomes load-bearing the moment comparative reading (the actual aggregate blocker — ADR-0131.G.2 / core-comparatives) can compose compare_additive/compare_multiplicative ops into an aggregate answer, and the lowercase-reference ("jar A" inside a later clause) resolves to the labeled entity.
• Compound enumeration and the recognizer-path labeled subject remain closed; open them only if a serving need is proven under the firewall.