Triple
T33082080
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FC Gueugnon |
E846533
|
entity |
| Predicate | scoreIn2000CoupeDeLaLigueFinal |
P175789
|
FINISHED |
| Object | 2–0 |
—
|
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: 2–0 | Statement: [FC Gueugnon, scoreIn2000CoupeDeLaLigueFinal, 2–0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: scoreIn2000CoupeDeLaLigueFinal Context triple: [FC Gueugnon, scoreIn2000CoupeDeLaLigueFinal, 2–0]
-
A.
coupeDeLaLigueTitle
Indicates that an entity has won a Coupe de la Ligue championship title.
-
B.
numberOfCoupeDeFranceTitles
Indicates the total count of Coupe de France titles that an entity has won.
-
C.
numberOfCoupeDeLaLigueTitles
Indicates the total count of Coupe de la Ligue titles that an entity has won.
-
D.
Ligue1TitleSeason
Indicates the relationship between a Ligue 1 football title and the specific season in which that title was won.
-
E.
worldCupFinalScore
Indicates the final score outcome of a FIFA World Cup match, typically specifying the number of goals each team scored by the end of the final game.
- F. None of above. chosen
Provenance (4 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_69f34954d46c8190a04a159cc5f99efd |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d74b20a48190900dda1014cc13a8 |
completed | May 3, 2026, 5:04 a.m. |
| PD | Predicate disambiguation | batch_69f6d27120988190aacec621cf2bf0e8 |
completed | May 3, 2026, 4:43 a.m. |
| PDg | Predicate description generation | batch_69f6d6a482fc8190b526291cd99b8696 |
completed | May 3, 2026, 5:01 a.m. |
Created at: May 1, 2026, 1:26 a.m.