Triple
T5409911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UNOCI |
E120988
|
entity |
| Predicate | peakUniformedPersonnel |
P63262
|
FINISHED |
| Object | over 10,000 |
—
|
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: over 10,000 | Statement: [UNOCI, peakUniformedPersonnel, over 10,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: peakUniformedPersonnel Context triple: [UNOCI, peakUniformedPersonnel, over 10,000]
-
A.
wearsUniformSimilarTo
Indicates that one entity wears a uniform that is similar in appearance or style to the uniform worn by another entity.
-
B.
wearsOnUniform
Indicates that an item is part of and is worn as a component of a uniform.
-
C.
policeCharacter
Indicates that one entity serves as a police officer or law-enforcement figure in relation to another entity.
-
D.
modernUniformDesigner
Indicates that an entity is the designer responsible for creating or developing a modern-style uniform for another entity.
-
E.
isHighestRankedUniformedOfficerIn
Indicates that one entity holds the top-ranking position among all uniformed officers within a specified organization or jurisdiction.
- 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_69bd463a41cc8190b32ff5af2b96ca93 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd8796a420819092c1771407cd1a5d |
completed | March 20, 2026, 5:44 p.m. |
| PD | Predicate disambiguation | batch_69bd8467e6b48190b9eaa9de67072e06 |
completed | March 20, 2026, 5:31 p.m. |
| PDg | Predicate description generation | batch_69bd865702ec8190831bde2c2a331f28 |
completed | March 20, 2026, 5:39 p.m. |
Created at: March 20, 2026, 2:05 p.m.