Do not stamp som_face on a mark that paired to nobody

_set_of_mark labels a detected face `unknown` when gated pairing matched it to no
present character. An answer pointing at that mark grounds nothing, yet it carried
som_face, the highest-trust provenance. All 7 som_face lines in the first 36
panels of the rerun were this case. Same defect class as the fake tail label.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

caveats/speaker-attribution.md

@@ -50,8 +50,14 @@ The registry holds 53 characters for manga `ef105a86`, 41 of them unnamed, with
 Haeseon and Seonho do. `/stage/clear` leaves the per-manga registry intact by design, so every rerun
 inherits the whole pile.
 
+Duplicate rows also make a correct name unresolvable. This manga holds `Choi Haeseon`, `Seonho` with
+aliases `["Lim Seonho", "Seonho"]`, and a separate `Lim Seonho`. An answer of "Lim Seonho" matches two
+rows, so `normalize_speaker` returns candidates and raises `ambiguous-speaker` instead of binding. The
+pipeline read the name correctly and still cannot name the speaker.
+
 **Revisit trigger:** before any run that is meant to produce a clean baseline. Either scope the
-registry to a chapter or add a reviewed reset.
+registry to a chapter or add a reviewed reset. Merging the duplicate rows needs the reversible-merge
+design first (`caveats/audit-open.md#destructive-reconcile`).
 
 ## One invented word still halts the chapter {#one-word-halts-chapter}
 

decisions/CLAUDE.md

@@ -31,4 +31,5 @@ still live belongs in `caveats/`.
 | [The orchestrator creates missing buckets at startup](storage-layout.md#ensure-buckets) | closed |
 | [RustFS is staged, not adopted](storage-layout.md#rustfs-staged) | open |
 | [A model guess is never labelled `tail`](speaker-attribution.md#no-fake-tail) | closed |
+| [The model's speaker answer is resolved against what the prompt showed](speaker-attribution.md#prompt-label-answers) | closed |
 | [Cast names enter the verifier tokenized](speaker-attribution.md#multiword-cast-names) | closed |

decisions/speaker-attribution.md

@@ -30,6 +30,30 @@ Check: `python worker_vision.py`, the `crowd`/`lone` cases.
 Cost: the named-speaker share will fall. The 30% headline was measured on attributions the sample says
 are wrong, so the lower number is the first honest one.
 
+## The model's speaker answer is resolved against what the prompt showed {#prompt-label-answers}
+
+**Closed.** `build_dialogue_prompt` renders a present character as
+`- person_1: brown ponytail, green dress (f)`. gemma answers with any part of that line, so every part
+of it must map back to the `local_id`. It did not, and `normalize_speaker` classified each unmatched
+answer as a free-form name that no registry entry could match.
+
+Evidence, measured on 36 panels of the cancelled first rerun. 15 lines carried a description and 9 a bare
+`local_id` with no identity assignment. 2 carried a stale `P1` mark label, 2 a name with the gender
+marker attached. That is 28 of 51 speech lines. Only 3 resolved to a `character_id`.
+
+`_apply_speaker_labels` now takes `present` and resolves the id, the name, the description, and each of
+those plus the gender marker. A key shared by two present characters is dropped, because it identifies
+neither. An id-shaped answer naming nobody present becomes `unknown`. A trailing gender marker is
+stripped, so an off-panel `Seonho (m)` still matches the registry name `Seonho`.
+
+`normalize_speaker` refuses an id-shaped value independently, because the worker is a separate process
+and the contract is load-bearing (invariant 7).
+
+Forbids: showing the model a label the worker cannot resolve back, and treating an unmatched speaker
+string as a name.
+Check: `python worker_vision.py`, the `shown`/`twins` cases. `pytest test_correctness.py`,
+`test_an_id_shaped_speaker_is_never_a_name`.
+
 ## Cast names enter the verifier tokenized {#multiword-cast-names}
 
 **Closed.** `verify_script` compares single capitalized tokens, so every allowed name must be present as

worker_vision.py

@@ -165,8 +165,14 @@ def _apply_speaker_labels(dialogue: list, label_map: dict, present: list | None
         if not s:
             continue
         if s in label_map:
-            d["speaker"] = label_map[s]
-            d["speaker_method"] = "som_face"
+            lid = label_map[s]
+            # _set_of_mark labels a detected face `unknown` when gated pairing matched it to no present
+            # character. An answer pointing at such a mark grounds nothing, so it must not carry
+            # `som_face`, the highest-trust label. 7 of 7 som_face lines on the first 36 panels of the
+            # 2026-08-11 rerun were this case.
+            d["speaker"] = lid
+            if lid != "unknown":
+                d["speaker_method"] = "som_face"
             continue
         bare = _GENDER_SUFFIX.sub("", s).strip()
         lid = keys.get(s.casefold()) or keys.get(bare.casefold())
@@ -1044,6 +1050,10 @@ if __name__ == "__main__":
     lbl = _apply_speaker_labels([{"speaker": "P1"}, {"speaker": "Aria"}, {"speaker": "unknown"}],
                                 {"P1": "person_3"})
     assert lbl[0]["speaker"] == "person_3" and lbl[1]["speaker"] == "Aria" and lbl[2]["speaker"] == "unknown"
+    assert lbl[0]["speaker_method"] == "som_face"
+    # a mark whose face paired to nobody present grounds nothing, so it gets no som_face label
+    unpaired = _apply_speaker_labels([{"speaker": "P2"}], {"P2": "unknown"})[0]
+    assert unpaired["speaker"] == "unknown" and "speaker_method" not in unpaired, unpaired
     # gemma answers with what the prompt SHOWED, not the id: description, name+gender, bare id, junk id.
     # Each shape cost real lines on job 778297bc by falling through as a free-form name.
     shown = [{"local_id": "person_1", "desc": "brown ponytail, green dress", "gender": "f"},