Triple
T6637722
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | France women’s national football team |
E150499
|
entity |
| Predicate | bestFifaWorldCupResult |
P58025
|
FINISHED |
| Object | fourth place |
—
|
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: fourth place | Statement: [France women’s national football team, bestFifaWorldCupResult, fourth place]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bestFifaWorldCupResult Context triple: [France women’s national football team, bestFifaWorldCupResult, fourth place]
-
A.
FIFABallonDorWinner
Indicates that the subject is the football player who won the FIFA Ballon d'Or award in the specified year or context.
-
B.
bestWorldCupResult
Indicates the most successful performance or highest achievement an entity has attained in any FIFA World Cup tournament.
-
C.
bestWorldCupPerformance
chosen
Indicates the highest level of achievement or furthest stage reached by an entity in any FIFA World Cup tournament.
-
D.
fifaU20WWC_bestResult
Indicates the best performance or highest stage a team has achieved in the FIFA U-20 Women's World Cup tournament.
-
E.
bestWorldCupResultYear
Indicates the year in which an entity achieved its best (highest) result in a World Cup tournament.
- 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_69c687f0ceb08190bf40807bfc605fa5 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6c308a08881908501c862b3029321 |
completed | March 27, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69c6ad024860819084b9b535b136ede6 |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:59 p.m.