Triple
T23742241
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KPMG Women’s PGA Championship |
E586706
|
entity |
| Predicate | numberOfMajorsInWomenGolf |
P8059
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [KPMG Women’s PGA Championship, numberOfMajorsInWomenGolf, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMajorsInWomenGolf Context triple: [KPMG Women’s PGA Championship, numberOfMajorsInWomenGolf, 5]
-
A.
majorChampionshipWins
chosen
Indicates the number of major championship titles an entity has won.
-
B.
numberOfFemaleAthletes
Indicates the count of athletes who are female in a given context or group.
-
C.
fedCupTitles
Indicates the number of Fed Cup (now Billie Jean King Cup) titles an entity has won.
-
D.
majorChampionship
Indicates that an entity has won or holds a significant, top-tier championship title in a competitive field.
-
E.
numberOfPGAChampionshipSpots
Indicates the quantity of qualification spots available for the PGA Championship associated with a given event or 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_69e24908efb08190bf755c3a9b91f222 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1bad734b08190a4b3365df97c73e1 |
completed | April 29, 2026, 8:01 a.m. |
| PD | Predicate disambiguation | batch_69f155f012808190a4b1cbc155558ade |
completed | April 29, 2026, 12:50 a.m. |
Created at: April 17, 2026, 7:11 p.m.