Triple
T12057558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 孫基禎 |
E287080
|
entity |
| Predicate | personalBestMarathonTime |
P43521
|
FINISHED |
| Object | 2:26:42 |
—
|
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: 2:26:42 | Statement: [孫基禎, personalBestMarathonTime, 2:26:42]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: personalBestMarathonTime Context triple: [孫基禎, personalBestMarathonTime, 2:26:42]
-
A.
personalBest_halfMarathon
Indicates that the associated half marathon performance is the best (fastest or highest-achieving) one recorded for the person.
-
B.
personalBest
chosen
Indicates that one entity represents the best performance or achievement ever attained personally by another entity.
-
C.
marathonWinner
Indicates that one entity is the competitor who finished first and won a specified marathon event.
-
D.
longDistanceRecordTime
Indicates the time value associated with a long-distance record performance or achievement.
-
E.
personalBestShortProgramEvent
Indicates the event in which an individual achieved their personal best score in the short program.
- 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_69d6ab4780948190bdb9f7620c2ac27e |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9100b4ca8819084845ca4c13e34ce |
completed | April 10, 2026, 2:58 p.m. |
| PD | Predicate disambiguation | batch_69d902bda47c8190b94860b31df4a98c |
completed | April 10, 2026, 2:01 p.m. |
Created at: April 8, 2026, 9:47 p.m.