Triple
T1188207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Byron Nelson |
E25295
|
entity |
| Predicate | majorWins |
P8059
|
FINISHED |
| Object | 5 major championships |
—
|
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 major championships | Statement: [Byron Nelson, majorWins, 5 major championships]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: majorWins Context triple: [Byron Nelson, majorWins, 5 major championships]
-
A.
majorChampionshipWins
chosen
Indicates the number of major championship titles an entity has won.
-
B.
mostOverallWinsRecord
Indicates that the subject holds the record for having the greatest total number of wins compared to all others in the relevant context.
-
C.
mostOverallWinsHolder
Indicates that the subject is the entity holding the highest total number of wins overall, compared to all other relevant entities.
-
D.
careerWins
Indicates the total number of wins an individual or entity has accumulated over the course of their entire career.
-
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_69a49427d98881908646d6c63b8cea1e |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd568cf481908d10cf19a3ce28f3 |
completed | March 1, 2026, 10:27 p.m. |
| PD | Predicate disambiguation | batch_69a4bb5bacc481909e8dfd5215e4711a |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:45 p.m.