Triple
T24195796
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LaVell Edwards |
E599831
|
entity |
| Predicate | numberOfLossesAsHeadCoach |
P8293
|
FINISHED |
| Object | 101 |
—
|
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: 101 | Statement: [LaVell Edwards, numberOfLossesAsHeadCoach, 101]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfLossesAsHeadCoach Context triple: [LaVell Edwards, numberOfLossesAsHeadCoach, 101]
-
A.
gamesWonAsHeadCoach
Indicates the number of games that an individual has won while serving in the role of head coach.
-
B.
careerLosses
chosen
Indicates the total number of defeats or losses an entity has accumulated over the course of its entire career.
-
C.
numberOfNBAChampionshipsAsHeadCoach
Indicates the count of NBA championship titles an individual has won while serving as a head coach.
-
D.
winningPercentageAsHeadCoach
Indicates the proportion of games a person has won while serving in the role of head coach.
-
E.
hasLosingTeamCoach
Indicates that a particular game, match, or competition is associated with the coach of the team that lost.
- 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_69e288ceaab88190899d0acb5931591d |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1e24ad83c819084ac9e34d2cc2120 |
completed | April 29, 2026, 10:49 a.m. |
| PD | Predicate disambiguation | batch_69f1c43e55688190b55fc20274ed471c |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 17, 2026, 11:36 p.m.