Triple
T37825875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Last Precinct |
E943056
|
entity |
| Predicate | hasMainProfessionOfSeriesHero |
P204411
|
FINISHED |
| Object | Chief Medical Examiner |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
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.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Chief Medical Examiner | Statement: [The Last Precinct, hasMainProfessionOfSeriesHero, Chief Medical Examiner]
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 (4 batches)
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. |
| NER | Named-entity recognition | batch_6a037cae084081909004d77514c5f286 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a1772e48190ba738c6d11b321e2 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c84ecbc81908232e5215355f43b |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:19 p.m.