Triple
T2682553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pat Summitt |
E57405
|
entity |
| Predicate | totalCareerWinsAsCollegeCoach |
P39702
|
FINISHED |
| Object | 1098 |
—
|
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: 1098 | Statement: [Pat Summitt, totalCareerWinsAsCollegeCoach, 1098]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalCareerWinsAsCollegeCoach Context triple: [Pat Summitt, totalCareerWinsAsCollegeCoach, 1098]
-
A.
coachingRecordCollegeWins
chosen
Indicates the number of games a coach has won at the college level in their coaching record.
-
B.
coachingRecordCollegeLosses
Indicates the number of games a coach’s college team has lost in their coaching record.
-
C.
championshipWonAsCoach
Indicates that the subject, acting in the role of coach, has won a championship title with the associated team or organization.
-
D.
careerNBARegularSeasonWinsAsCoach
Indicates the total number of NBA regular season games a person has won in their role as a head coach.
-
E.
collegeTeamCoached
Indicates that a person has served as a coach for a particular college sports team.
- 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_69ab4a5028388190a36f3baf1588309e |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd9d602848190b638e417e710a555 |
completed | March 7, 2026, 7:55 a.m. |
| PD | Predicate disambiguation | batch_69abd81c9b4c81908e5e0da6ac5f828b |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:54 p.m.