Triple
T16293267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SM Caen |
E395579
|
entity |
| Predicate | league |
P888
|
FINISHED |
| Object | Ligue 2 |
E186351
|
NE 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: Ligue 2 | Statement: [SM Caen, league, Ligue 2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ligue 2 Context triple: [SM Caen, league, Ligue 2]
-
A.
Ligue 2
chosen
Ligue 2 is the second tier of professional football in the French league system, sitting directly below Ligue 1.
-
B.
Ligue 1 and Ligue 2
Ligue 1 and Ligue 2 are the top two professional divisions of French football, forming the elite tiers of the national league system.
-
C.
Ligue A (France)
Ligue A (France) is the top professional men's volleyball league in France, featuring the country's leading clubs in the sport.
-
D.
Ligue 1
Ligue 1 is France’s top professional football division, featuring the country’s leading clubs in the highest tier of its league system.
-
E.
Ligue 1
Ligue 1 is the top professional football division in Tunisia, featuring the country’s leading clubs in the national league system.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d87f22c7248190a54c949738441e2e |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e25e2aee6881909fd28547f135427c |
completed | April 17, 2026, 4:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0025fd56008190a9cb896e7ea2d26a |
completed | May 10, 2026, 6:30 a.m. |
Created at: April 10, 2026, 5:05 a.m.