Triple
T105790
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japan Standard Time |
E2133
|
entity |
| Predicate | previouslyHadMultipleLocalTimes |
P1832
|
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, previouslyHadMultipleLocalTimes, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: previouslyHadMultipleLocalTimes Context triple: [Japan Standard Time, previouslyHadMultipleLocalTimes, true]
-
A.
hasTimeZones
Indicates that an entity is associated with one or more time zones in which it is valid or operates.
-
B.
timeZoneHistorical
chosen
Indicates that there is a historical time zone relationship, specifying how an entity’s time zone (including offsets, rules, or changes) applied during past periods.
-
C.
locatedInTimeZone
Indicates that an entity exists or an event occurs within the temporal bounds defined by a specific time zone.
-
D.
observesSameTimeAs
Indicates that two observation events occur simultaneously or during the same time interval.
-
E.
timeZoneType
Indicates the classification or category of a time zone associated with an entity (e.g., standard, daylight, or specific time zone format/type).
- 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_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. |
Created at: Feb. 28, 2026, 2:12 a.m.