Triple
T33082081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FC Gueugnon |
E846533
|
entity |
| Predicate | leagueLevelAt2000CoupeDeLaLigueWin |
P179857
|
FINISHED |
| Object | Ligue 2 |
—
|
NE NERFINISHED |
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: Ligue 2 | Statement: [FC Gueugnon, leagueLevelAt2000CoupeDeLaLigueWin, Ligue 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leagueLevelAt2000CoupeDeLaLigueWin Context triple: [FC Gueugnon, leagueLevelAt2000CoupeDeLaLigueWin, Ligue 2]
-
A.
numberOfCoupeDeLaLigueTitles
Indicates the total count of Coupe de la Ligue titles that an entity has won.
-
B.
coupeDeLaLigueTitle
Indicates that an entity has won a Coupe de la Ligue championship title.
-
C.
Ligue1Titles
Indicates the number of Ligue 1 championship titles 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.
hasWonTopFlightLeagueInFrance
Indicates that the subject has won the championship title of the highest professional football league in France at least once.
- 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_69f727afd5d88190ad48735cd1b32787 |
completed | May 3, 2026, 10:47 a.m. |
| PD | Predicate disambiguation | batch_69f72737c42c8190a3f781a5e98868ff |
completed | May 3, 2026, 10:45 a.m. |
| PDg | Predicate description generation | batch_69f727ad9ff88190ba8069dd48b2e98f |
completed | May 3, 2026, 10:47 a.m. |
Created at: May 1, 2026, 1:26 a.m.