Triple
T10442429
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Tellez |
E246200
|
entity |
| Predicate | hasWonAsCoach |
P24378
|
FINISHED |
| Object | Olympic gold medals through his athletes |
—
|
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: Olympic gold medals through his athletes | Statement: [Tom Tellez, hasWonAsCoach, Olympic gold medals through his athletes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWonAsCoach Context triple: [Tom Tellez, hasWonAsCoach, Olympic gold medals through his athletes]
-
A.
championshipWonAsCoach
Indicates that the subject, acting in the role of coach, has won a championship title with the associated team or organization.
-
B.
notableAchievementAsCoach
Indicates that the subject has a significant or distinguished accomplishment specifically in their role as a coach.
-
C.
medalWonAsCoach
chosen
Indicates that an individual has won a medal in the role of a coach rather than as a competitor.
-
D.
hasCoachedFor
Indicates that one entity has served in a coaching role for another entity, such as a team, organization, or individual.
-
E.
gamesWonAsHeadCoach
Indicates the number of games that 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_69d381c04fe08190957c26c526a3b05a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4fe083cd881909d2d8ad75d1d94cb |
completed | April 7, 2026, 12:52 p.m. |
| PD | Predicate disambiguation | batch_69d4fb73a5e48190a8df4775bc5da80f |
completed | April 7, 2026, 12:41 p.m. |
Created at: April 6, 2026, 12:15 p.m.