Triple
T4419177
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dan Reeves |
E95053
|
entity |
| Predicate | NFLHeadCoachingWins |
P39705
|
FINISHED |
| Object | 190 |
—
|
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: 190 | Statement: [Dan Reeves, NFLHeadCoachingWins, 190]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: NFLHeadCoachingWins Context triple: [Dan Reeves, NFLHeadCoachingWins, 190]
-
A.
coachingRecordNFLWins
chosen
Indicates the number of NFL games a coach has won in their coaching career.
-
B.
NFLHeadCoachRecordIncludes
Indicates that a particular NFL head coach’s record includes a specified game, season, statistic, or outcome as part of their official coaching history.
-
C.
NFLTeamCoached
Indicates that a person has served as a coach for a specific NFL team.
-
D.
overallNFLHeadCoachingRecordPostseason
Indicates the win-loss-tie record a head coach has accumulated in NFL postseason (playoff) games.
-
E.
numberOfSuperBowlsWonAsHeadCoach
Indicates the total count of Super Bowl championships an individual has won while serving in the role of head coach.
- 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_69b3453a36908190b95a79a297ca083c |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3551e7c6c819090fa5dfb5ac58e4c |
completed | March 13, 2026, 12:06 a.m. |
| PD | Predicate disambiguation | batch_69b34f5d0c54819085c08533bb58030a |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:29 p.m.