Triple
T30442457
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tokyo (Narita) – Hong Kong |
E774486
|
entity |
| Predicate | timeZoneOffsetOrigin |
P170224
|
FINISHED |
| Object | UTC+09:00 |
—
|
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: UTC+09:00 | Statement: [Tokyo (Narita) – Hong Kong, timeZoneOffsetOrigin, UTC+09:00]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeZoneOffsetOrigin Context triple: [Tokyo (Narita) – Hong Kong, timeZoneOffsetOrigin, UTC+09:00]
-
A.
timeZoneOriginDST
Indicates that the relationship specifies the original time zone of an entity, including whether daylight saving time (DST) is in effect.
-
B.
timeOffsetReference
Indicates that a temporal value is specified relative to a particular reference time or event, defining the offset between them.
-
C.
timeOffsetType
Indicates the type or category of temporal offset that specifies how one time point is shifted relative to another.
-
D.
timeOffsetInHours
Indicates the temporal difference between two time points or events, measured in hours.
-
E.
timezoneBasis
Indicates that one timezone is defined, measured, or interpreted in reference to another timezone as its underlying standard or point of comparison.
- 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_69f22493ef9c8190ae8c2afcb7f994c8 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f68b121eac81909e90416207bc1157 |
completed | May 2, 2026, 11:38 p.m. |
| PD | Predicate disambiguation | batch_69f6860def1c81909d79e1f088c4b5e5 |
completed | May 2, 2026, 11:17 p.m. |
| PDg | Predicate description generation | batch_69f68a160374819084d720985f800dfc |
completed | May 2, 2026, 11:34 p.m. |
Created at: April 29, 2026, 8:08 p.m.