Triple
T35068991
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cercle Saint-Pierre Limoges |
E1011813
|
entity |
| Predicate | FrenchCupTitles |
P55374
|
FINISHED |
| Object | multiple French Cup titles |
—
|
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: multiple French Cup titles | Statement: [Cercle Saint-Pierre Limoges, FrenchCupTitles, multiple French Cup titles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: FrenchCupTitles Context triple: [Cercle Saint-Pierre Limoges, FrenchCupTitles, multiple French Cup titles]
-
A.
FrenchLeagueTitlesWith
Indicates a relationship where two entities are associated through having won French football league titles together (e.g., a club and a player sharing those titles).
-
B.
numberOfCoupeDeFranceTitles
chosen
Indicates the total count of Coupe de France titles that an entity has won.
-
C.
FrenchChampionshipTitle
Indicates that an entity has won a championship title in a French competition or league.
-
D.
CoupeDeFranceTitleSeason
Indicates the specific season in which a Coupe de France title was won.
-
E.
Ligue1Titles
Indicates the number of Ligue 1 championship titles an entity has won.
- 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_69f76dd193108190af2528186f25b72a |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78ce78b508190955848e133398dc8 |
completed | May 3, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69f78b8f4cc08190b49fccd798cb25d7 |
completed | May 3, 2026, 5:53 p.m. |
Created at: May 3, 2026, 4:01 p.m.