Triple
T2475478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don Coryell |
E55077
|
entity |
| Predicate | coachingRecordCollegeWins |
P39702
|
FINISHED |
| Object | 104 |
—
|
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: 104 | Statement: [Don Coryell, coachingRecordCollegeWins, 104]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coachingRecordCollegeWins Context triple: [Don Coryell, coachingRecordCollegeWins, 104]
-
A.
collegeTeamCoached
Indicates that a person has served as a coach for a particular college sports team.
-
B.
playedCollegeTeam
Indicates that an athlete was a member of and competed for a particular college sports team.
-
C.
scoredPointsPerGameInCollege
Indicates the average number of points an entity (typically an athlete) scored per game during their college career.
-
D.
collegeSportsContext
Indicates a relationship, event, or situation that specifically occurs within or is shaped by the environment of college or university sports.
-
E.
coachingRecordNFLPostseasonWins
Indicates the number of postseason (playoff) games an NFL coach has won in their coaching career.
- F. None of above. chosen
Provenance (4 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_69ab49e279e88190ab10d7248aea9d11 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd1eb3be481908fa7c6b8f1c78209 |
completed | March 7, 2026, 7:21 a.m. |
| PD | Predicate disambiguation | batch_69abd0b5e3d481909a5cbc4a96edd24f |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd1e45380819094b3f32a278bd457 |
completed | March 7, 2026, 7:21 a.m. |
Created at: March 6, 2026, 9:45 p.m.