Triple
T9487902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Afghanistan Time |
E228807
|
entity |
| Predicate | hasFixedOffsetYearRound |
P4886
|
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: [Afghanistan Time, hasFixedOffsetYearRound, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFixedOffsetYearRound Context triple: [Afghanistan Time, hasFixedOffsetYearRound, true]
-
A.
isFixedOffsetSince
Indicates that one entity consistently occurs or is positioned at a constant, unchanging offset in time or value relative to another reference entity.
-
B.
isFixedOffsetFromUTC
chosen
Indicates that one time-related entity consistently differs from Coordinated Universal Time (UTC) by a specific, unchanging offset.
-
C.
hasTimeOffset
Indicates that one temporal value is shifted or displaced from another by a specified amount of time.
-
D.
hasNonIntegerHourOffset
Indicates that the time-related value or time zone differs from a reference time by an offset that is not a whole number of hours (e.g., includes 30- or 45-minute increments).
-
E.
usesJulianCalendar
Indicates that the subject follows or is based on the Julian calendar system for dating events or timekeeping.
- 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_69ca847424f081908180305555139f7a |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd80c443b88190968d2092a73e1ee4 |
completed | April 1, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69cca561e6b0819090aa795f3c3a2083 |
completed | April 1, 2026, 4:56 a.m. |
Created at: March 30, 2026, 7:55 p.m.