Triple
T2475480
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don Coryell |
E55077
|
entity |
| Predicate | coachingRecordCollegeTies |
P39704
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Don Coryell, coachingRecordCollegeTies, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coachingRecordCollegeTies Context triple: [Don Coryell, coachingRecordCollegeTies, 2]
-
A.
collegeTeamCoached
Indicates that a person has served as a coach for a particular college sports team.
-
B.
hasCoachedFor
Indicates that one entity has served in a coaching role for another entity, such as a team, organization, or individual.
-
C.
workedForCollegeTeam
Indicates that an individual was employed by or served in a working role for a college sports team.
-
D.
notableAssistantCoaches
Indicates that the subject has assistant coaches who are particularly distinguished or noteworthy in their roles.
-
E.
hasAffiliatedCollegesIn
Indicates that an institution maintains affiliated colleges located within a specified geographic area or jurisdiction.
- 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.