Triple
T31083467
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Muñoz |
E792164
|
entity |
| Predicate | playedProfessionalFootball |
P109980
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Michael Muñoz, playedProfessionalFootball, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: playedProfessionalFootball Context triple: [Michael Muñoz, playedProfessionalFootball, yes]
-
A.
playedAssociationFootball
Indicates that an entity has participated in playing the sport of association football (soccer).
-
B.
playedAmericanFootball
chosen
Indicates that one entity participated in playing American football, either professionally, collegiately, or at another organized level, during some period of time.
-
C.
playsFootballCode
Indicates that an entity participates in or is involved with a specific code or variant of football (e.g., soccer, rugby, American football).
-
D.
playsAssociationFootballIn
Indicates that an entity participates in playing association football (soccer) within or for a specified place, organization, or context.
-
E.
hasPlayedProfessionalSports
Indicates that an entity has participated as an athlete in an officially recognized professional-level sports competition or league.
- 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_69f224ce48348190bd0fc23f656ed683 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f760a35b988190904e6267553ad2fe |
completed | May 3, 2026, 2:50 p.m. |
| PD | Predicate disambiguation | batch_69f75eb3d6f081908c933474eb359e3d |
completed | May 3, 2026, 2:41 p.m. |
Created at: April 29, 2026, 9:02 p.m.