Triple
T37825875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Last Precinct |
E943056
|
entity |
| Predicate | hasMainProfessionOfSeriesHero |
—
|
GENERATED |
| Object | Chief Medical Examiner |
—
|
UNRECOGNIZED GENERATED |
How this triple was built (1 step)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMainProfessionOfSeriesHero Context triple: [The Last Precinct, hasMainProfessionOfSeriesHero, Chief Medical Examiner]
-
A.
hasMainRole
Indicates that an entity holds the primary or most significant role in relation to another entity or context.
-
B.
hasMainCharacterFrom
Indicates that a work of fiction has a main character who originates from or belongs to a specified place, group, or source.
-
C.
hasCoProtagonistOccupation
Indicates that two or more co-protagonists share a specified occupation or professional role.
-
D.
hasMainProtagonistTrait
Indicates that the specified trait is a defining or primary characteristic of the main protagonist.
-
E.
hasEponymousHero
Indicates that a work or narrative features a hero whose name is the same as, or gives its name to, the work itself.
- F. None of above. chosen
Provenance (1 batch)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f76eea4c8c8190a335aed5955cf2db |
completed | May 3, 2026, 3:51 p.m. |
Created at: May 3, 2026, 4:19 p.m.