Triple
T2833814
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Olympique Lyonnais Féminin |
E62301
|
entity |
| Predicate | hasWonDomesticCup |
P41925
|
FINISHED |
| Object | French women's cup |
—
|
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: French women's cup | Statement: [Olympique Lyonnais Féminin, hasWonDomesticCup, French women's cup]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWonDomesticCup Context triple: [Olympique Lyonnais Féminin, hasWonDomesticCup, French women's cup]
-
A.
hasWonDomesticCupsAsManager
Indicates that a person, in their role as a manager, has achieved victories in domestic cup competitions.
-
B.
wonLeagueCup
Indicates that a team or competitor achieved victory in a specific league cup competition or tournament.
-
C.
domesticCupTitles
Indicates the number of national (in-country) cup competitions a team or club has won.
-
D.
nationalCupTitle
chosen
Indicates that an entity has won a national-level cup competition title in a given sport or domain.
-
E.
wonScottishCup
Indicates that the subject achieved victory in the Scottish Cup 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_69ab4c3c39188190955b9c49d98463d8 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdec18b808190aedae2ed11d53b15 |
completed | March 7, 2026, 8:16 a.m. |
| PD | Predicate disambiguation | batch_69abdd0ce8b08190ba28c192988f38ce |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:01 p.m.