Triple
T973108
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York City auxiliary police officers |
E20987
|
entity |
| Predicate | wearsUniform |
P271
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [New York City auxiliary police officers, wearsUniform, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wearsUniform Context triple: [New York City auxiliary police officers, wearsUniform, true]
-
A.
wearsOnUniform
Indicates that an item is part of and is worn as a component of a uniform.
-
B.
wears
chosen
Indicates that one entity is dressed in, or has on its body, a particular item such as clothing or accessories.
-
C.
hasInsigniaWornBy
Indicates that a particular insignia is worn by a specified entity (such as a person, group, or organization).
-
D.
partOfOfficerLine
Indicates that one entity is a member or segment of a sequence or formation of officers.
-
E.
wearingClass
Indicates that one entity is wearing or dressed in an item belonging to a particular class or category of clothing or accessories.
- F. None of above.
Provenance (3 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_69a493b33d2c81909c52c369d3ca8436 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b45f28f081908d41b2d7f353708d |
completed | March 1, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a6aa2c8190aebba71320ab678f |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.