Triple
T8013882
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matt LaFleur |
E186561
|
entity |
| Predicate | hasCoachedInPlayoffs |
P30583
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Matt LaFleur, hasCoachedInPlayoffs, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCoachedInPlayoffs Context triple: [Matt LaFleur, hasCoachedInPlayoffs, yes]
-
A.
coachedTeamToPlayoffs
chosen
Indicates that a coach successfully led a team to qualify for the playoffs in a competition or league.
-
B.
hasCoachedFor
Indicates that one entity has served in a coaching role for another entity, such as a team, organization, or individual.
-
C.
hasCoachedProfessionalSports
Indicates that a person has served in a coaching role for a professional-level sports team or athlete.
-
D.
hasCoachedInSuperBowl
Indicates that a person has served as a coach in at least one Super Bowl game.
-
E.
hasCoachedCompetition
Indicates that one entity has served as a coach for another entity in the context of a specific competition or contest.
- 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_69ca82abaffc8190ab8af79cdbc31ab3 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3df0f4bc8190ae87586972018085 |
completed | March 31, 2026, 3:22 a.m. |
| PD | Predicate disambiguation | batch_69cb048c9f488190b4fb8917a9c21bc5 |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:19 p.m.