Triple
T12173588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brooke Henderson |
E290033
|
entity |
| Predicate | professionalWinCountApprox |
P71302
|
FINISHED |
| Object | more than 10 LPGA Tour victories |
—
|
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: more than 10 LPGA Tour victories | Statement: [Brooke Henderson, professionalWinCountApprox, more than 10 LPGA Tour victories]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionalWinCountApprox Context triple: [Brooke Henderson, professionalWinCountApprox, more than 10 LPGA Tour victories]
-
A.
careerWins
Indicates the total number of wins an individual or entity has accumulated over the course of their entire career.
-
B.
careerManagerialWins
Indicates the total number of games or contests an individual has won in a managerial role over the course of their entire career.
-
C.
professionalWins
chosen
Indicates that one entity has achieved a certain number of victories or successes in a professional context, such as in a career, competition, or formal domain.
-
D.
careerWinLossRecord
Indicates the overall tally of wins and losses an entity has accumulated over the entire span of its career.
-
E.
professionalRecordDraws
Indicates the number of times a professional competitor’s official matches have ended in a draw.
- 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_69d6ab4d6c00819095a9a7c35de83cfb |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91621ca6c81908365732f361aef13 |
completed | April 10, 2026, 3:24 p.m. |
| PD | Predicate disambiguation | batch_69d9150e85348190b9b47cda4a17dcd0 |
completed | April 10, 2026, 3:19 p.m. |
Created at: April 8, 2026, 9:50 p.m.