Triple
T23959891
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seychelles Time |
E603895
|
entity |
| Predicate | isSimilarOffsetTo |
P154049
|
FINISHED |
| Object | Gulf Standard Time |
—
|
NE NERFINISHED |
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: Gulf Standard Time | Statement: [Seychelles Time, isSimilarOffsetTo, Gulf Standard Time]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isSimilarOffsetTo Context triple: [Seychelles Time, isSimilarOffsetTo, Gulf Standard Time]
-
A.
isOffsetFrom
Indicates that one entity’s position, value, or occurrence is displaced by a specified amount or direction relative to another entity.
-
B.
hasSimilarityTo
Indicates that one entity shares common characteristics, features, or qualities with another entity to a notable degree.
-
C.
isCloseTo
Indicates that one entity is physically or conceptually near another, within a relatively short distance or range.
-
D.
usesOffsetFrom
Indicates that one entity determines or expresses its position, value, or behavior relative to another by applying a specified offset from that reference.
-
E.
isUnusualOffset
Indicates that one value or position deviates from the expected or standard offset relative to another.
- 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_69e2954222288190a7323554d0cca8d7 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1d0d91cd481908f53ce7ee569c0f9 |
completed | April 29, 2026, 9:35 a.m. |
| PD | Predicate disambiguation | batch_69f161578d54819084a8b35496299993 |
completed | April 29, 2026, 1:39 a.m. |
| PDg | Predicate description generation | batch_69f167dca3608190ace9d2eef56b2af6 |
completed | April 29, 2026, 2:07 a.m. |
Created at: April 17, 2026, 9:23 p.m.