Triple
T6611628
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seve Ballesteros |
E149250
|
entity |
| Predicate | totalProfessionalWins |
P8292
|
FINISHED |
| Object | 90 |
—
|
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: 90 | Statement: [Seve Ballesteros, totalProfessionalWins, 90]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalProfessionalWins Context triple: [Seve Ballesteros, totalProfessionalWins, 90]
-
A.
careerWins
chosen
Indicates the total number of wins an individual or entity has accumulated over the course of their entire career.
-
B.
numberOfProfessionalFights
Indicates the total count of professional-level fights associated with an entity (such as a person or competitor).
-
C.
careerManagerialWins
Indicates the total number of games or contests an individual has won in a managerial role over the course of their entire career.
-
D.
careerWinLossRecord
Indicates the overall tally of wins and losses an entity has accumulated over the entire span of its career.
-
E.
mostGamesWonBy
Indicates that one entity holds the record for having won the greatest number of games compared to others 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_69c687ebc680819094caf71faba2efe2 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6cf3796d08190a26e988386089447 |
completed | March 27, 2026, 6:40 p.m. |
| PD | Predicate disambiguation | batch_69c6acfed25481909cac74c84a9fe088 |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:57 p.m.