Triple
T22837694
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Korean |
E565990
|
entity |
| Predicate | associatedWithTimeOffset |
P67827
|
FINISHED |
| Object | UTC+9 |
—
|
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+9 | Statement: [South Korean, associatedWithTimeOffset, UTC+9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithTimeOffset Context triple: [South Korean, associatedWithTimeOffset, UTC+9]
-
A.
associatedWithTime
Indicates a relationship where something is linked or connected to a specific point or period in time.
-
B.
hasTimeOffset
chosen
Indicates that one temporal value is shifted or displaced from another by a specified amount of time.
-
C.
timeOffsetReference
Indicates that a temporal value is specified relative to a particular reference time or event, defining the offset between them.
-
D.
associatedWithMatch
Indicates a relationship where an entity is linked or connected to a particular match or matching event.
-
E.
timeOffsetEffect
Indicates how a change or offset in time influences or modifies the occurrence, state, or outcome of another event or process.
- 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_69e245869e188190a196584f36e682da |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17e303cec81909c5c118dc8c93354 |
completed | April 29, 2026, 3:42 a.m. |
| PD | Predicate disambiguation | batch_69eed2d117088190acbfe130d84f8627 |
completed | April 27, 2026, 3:06 a.m. |
Created at: April 17, 2026, 3:35 p.m.