Triple
T15794285
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chess Oscar |
E382935
|
entity |
| Predicate | numberOfAwardsWonByGarryKasparov |
P3160
|
FINISHED |
| Object | 11 |
—
|
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: 11 | Statement: [Chess Oscar, numberOfAwardsWonByGarryKasparov, 11]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfAwardsWonByGarryKasparov Context triple: [Chess Oscar, numberOfAwardsWonByGarryKasparov, 11]
-
A.
yearAwardedGrandmasterTitle
Indicates the specific year in which an entity was officially awarded the grandmaster title.
-
B.
RussianChampionshipWinner
Indicates that the subject is the winner of a Russian national championship in a specified field or competition.
-
C.
numberOfAwards
chosen
Indicates the total count of awards that have been received by an entity.
-
D.
mostGamesWonBy
Indicates that one entity holds the record for having won the greatest number of games compared to others in a given context.
-
E.
gamesWonBy
Indicates the number of games that have been won by a particular entity in a given context.
- 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_69d86da16e188190b89af699f1ed0bfe |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0b4db55308190875a04f982c44cea |
completed | April 16, 2026, 10:07 a.m. |
| PD | Predicate disambiguation | batch_69e00537bd1c81908d6e832792fd934f |
completed | April 15, 2026, 9:37 p.m. |
Created at: April 10, 2026, 4:48 a.m.