Triple
T12639686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coupe de France |
E301859
|
entity |
| Predicate | numberOfTeamsPerSeasonApprox |
P6986
|
FINISHED |
| Object | 7000 |
—
|
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: 7000 | Statement: [Coupe de France, numberOfTeamsPerSeasonApprox, 7000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTeamsPerSeasonApprox Context triple: [Coupe de France, numberOfTeamsPerSeasonApprox, 7000]
-
A.
numberOfSeasonsWithTeam
Indicates the total count of seasons an entity (e.g., a player or coach) has spent with a particular team.
-
B.
numberOfTeamsVariesByYear
Indicates that the number of teams involved changes depending on the specific year.
-
C.
NLTeamsPerSeason
Indicates the association between a National League team and the specific season in which it participated.
-
D.
numberOfPromotedTeamsPerSeason
Indicates the number of teams that are promoted to a higher division or league in each season.
-
E.
hasNumberOfTeams
chosen
Indicates the quantity of teams associated with or contained by a given entity.
- 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_69d7bdec9f9c8190b4bac675b7588211 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961ae493481908f82e0d05dce20bd |
completed | April 10, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69d960b47130819097e1162ed4fc993a |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:16 p.m.