Triple
T4595895
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lille OSC |
E100202
|
entity |
| Predicate | hasWonTopFlightLeagueInFrance |
P58198
|
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: [Lille OSC, hasWonTopFlightLeagueInFrance, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWonTopFlightLeagueInFrance Context triple: [Lille OSC, hasWonTopFlightLeagueInFrance, yes]
-
A.
numberOfCoupeDeFranceTitles
Indicates the total count of Coupe de France titles that an entity has won.
-
B.
Ligue1TitleSeason
Indicates the relationship between a Ligue 1 football title and the specific season in which that title was won.
-
C.
numberOfCoupeDeLaLigueTitles
Indicates the total count of Coupe de la Ligue titles that an entity has won.
-
D.
wonChampionsLeagueAsPlayer
Indicates that the subject has been part of a team that won the UEFA Champions League in the role of a player.
-
E.
numberOfTropheeDesChampionsTitles
Indicates the number of Trophée des Champions titles that an entity has won.
- 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_69bd43cbc014819098b45f435908f88a |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd593f25888190a4f219e4da494764 |
completed | March 20, 2026, 2:27 p.m. |
| PD | Predicate disambiguation | batch_69bd522c811c81909aae4feadae33174 |
completed | March 20, 2026, 1:57 p.m. |
| PDg | Predicate description generation | batch_69bd56b4a9508190acdb888eef18f1ee |
completed | March 20, 2026, 2:16 p.m. |
Created at: March 20, 2026, 1:11 p.m.