Triple
T2362800
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | C.F. Monterrey |
E47311
|
entity |
| Predicate | hasWonInternationalTitle |
P313
|
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: [C.F. Monterrey, hasWonInternationalTitle, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWonInternationalTitle Context triple: [C.F. Monterrey, hasWonInternationalTitle, yes]
-
A.
hasClaimedNationalTitlesInEra
Indicates that an entity has won or been awarded national titles during a specified historical period or era.
-
B.
worldChampionshipTitles
Indicates the number of world championship titles an entity has won.
-
C.
hasChampionships
chosen
Indicates that one entity possesses or has won one or more championships associated with another entity.
-
D.
wonTitleFrom
Indicates that one entity obtained a title or championship by defeating or surpassing another specific entity who previously held it.
-
E.
numberOfTitleDefenses
Indicates the number of times an entity has successfully defended a previously won title or championship.
- 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_69a88a1a4a6081908645b0f2914521ab |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abc74501388190adce9b3e51a03ded |
completed | March 7, 2026, 6:35 a.m. |
| PD | Predicate disambiguation | batch_69abc599b92c819093d9e15d4437705d |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:55 p.m.