Triple
T6896149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jewish Settlement Police |
E159173
|
entity |
| Predicate | uniformed |
P62728
|
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: [Jewish Settlement Police, uniformed, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: uniformed Context triple: [Jewish Settlement Police, uniformed, true]
-
A.
wearsOnUniform
Indicates that an item is part of and is worn as a component of a uniform.
-
B.
wearsUniformSimilarTo
Indicates that one entity wears a uniform that is similar in appearance or style to the uniform worn by another entity.
-
C.
usualAttire
chosen
Indicates the type of clothing an entity typically wears in ordinary or characteristic situations.
-
D.
modernUniformDesigner
Indicates that an entity is the designer responsible for creating or developing a modern-style uniform for another entity.
-
E.
peakUniformedPersonnel
Indicates the maximum number of uniformed personnel present or deployed at any point in time within a given context or operation.
- 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_69c6883568c8819081db6407e892cccc |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d95ae3f88190b7f5d440f90ae9f9 |
completed | March 27, 2026, 7:24 p.m. |
| PD | Predicate disambiguation | batch_69c6d7b7681481909ec50509b19fcf81 |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:24 p.m.