Triple
T105788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japan Standard Time |
E2133
|
entity |
| Predicate | usedUniformlyAcrossCountry |
P4880
|
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: [Japan Standard Time, usedUniformlyAcrossCountry, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedUniformlyAcrossCountry Context triple: [Japan Standard Time, usedUniformlyAcrossCountry, true]
-
A.
usedInCountry
Indicates that something is utilized, applied, or in operation within the specified country.
-
B.
usesUniform
Indicates that one entity regularly wears or employs a standardized set of clothing or equipment designated as a uniform.
-
C.
uniformDistinction
Indicates that a clear and consistent difference is maintained between two or more entities within a given context.
-
D.
usedInRegion
Indicates that something is utilized or applied within a specific geographic or administrative region.
-
E.
usedInCity
Indicates that something is utilized, applied, or operates within the context or boundaries of a particular city.
- 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_69a24e0a5b7c81908d52da08c60dabc4 |
completed | Feb. 28, 2026, 2:08 a.m. |
| NER | Named-entity recognition | batch_69a256ec650c8190bee2067e37065527 |
completed | Feb. 28, 2026, 2:46 a.m. |
| PD | Predicate disambiguation | batch_69a2563d33788190999d471b486d5603 |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a256ea776081908fec36c3fdfb8d84 |
completed | Feb. 28, 2026, 2:46 a.m. |
Created at: Feb. 28, 2026, 2:12 a.m.