Triple
T12813298
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2015 FIBA Americas Women's Championship |
E306323
|
entity |
| Predicate | qualificationSpotCountForOlympics |
P67249
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [2015 FIBA Americas Women's Championship, qualificationSpotCountForOlympics, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: qualificationSpotCountForOlympics Context triple: [2015 FIBA Americas Women's Championship, qualificationSpotCountForOlympics, 1]
-
A.
qualificationSpotsForOlympics
chosen
Indicates the number of available qualification positions that grant entry to the Olympic Games.
-
B.
capacityDuringOlympics
Indicates the seating or usage capacity of a venue, facility, or service specifically during the Olympic Games period.
-
C.
olympicGamesNumber
Indicates the specific edition number assigned to a particular occurrence of the Olympic Games.
-
D.
estimatedNumberOfOlim
Indicates the estimated count of individuals who have made or will make aliyah (immigrate to Israel) in a given context.
-
E.
openedForOlympics
Indicates that something was opened or inaugurated specifically in preparation for or in conjunction with the Olympic Games.
- 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_69d7bdf46c448190b1faa55aaacb6317 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e9adcf08190a12801adcc613477 |
completed | April 10, 2026, 9:41 p.m. |
| PD | Predicate disambiguation | batch_69d9640ed7448190b276e7fab649f7d2 |
completed | April 10, 2026, 8:56 p.m. |
Created at: April 9, 2026, 5:31 p.m.