Triple
T8005921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luis de la Fuente |
E186362
|
entity |
| Predicate | wonMedalAsCoach |
P24378
|
FINISHED |
| Object | Olympic silver medal in men’s football |
—
|
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 silver medal in men’s football | Statement: [Luis de la Fuente, wonMedalAsCoach, Olympic silver medal in men’s football]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wonMedalAsCoach Context triple: [Luis de la Fuente, wonMedalAsCoach, Olympic silver medal in men’s football]
-
A.
medalWonAsCoach
chosen
Indicates that an individual has won a medal in the role of a coach rather than as a competitor.
-
B.
notableAchievementAsCoach
Indicates that the subject has a significant or distinguished accomplishment specifically in their role as a coach.
-
C.
championshipWonAsCoach
Indicates that the subject, acting in the role of coach, has won a championship title with the associated team or organization.
-
D.
hasCoachedFor
Indicates that one entity has served in a coaching role for another entity, such as a team, organization, or individual.
-
E.
wonMedalAt
Indicates that an entity received a medal as a result of participating in a specific event or competition.
- 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_69ca82aaaf24819084b94d18f699ba53 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3cf72fc08190aa78b97c1ab92f90 |
completed | March 31, 2026, 3:18 a.m. |
| PD | Predicate disambiguation | batch_69cb048c9f488190b4fb8917a9c21bc5 |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:18 p.m.