Triple
T2507683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | France national football team |
E52622
|
entity |
| Predicate | fifaWorldCupTitles |
P34505
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [France national football team, fifaWorldCupTitles, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fifaWorldCupTitles Context triple: [France national football team, fifaWorldCupTitles, 2]
-
A.
WorldCupOverallTitles
Indicates the total number of World Cup championship titles an entity has won across all tournaments.
-
B.
fifaWorldCupAppearances
Indicates the number of times an entity has participated in FIFA World Cup tournaments.
-
C.
WorldCupSeasonTitles
Indicates the number of World Cup titles an entity has won in a given season or across seasons.
-
D.
WorldCupWins
chosen
Indicates the number of times an entity (typically a national team) has won the FIFA World Cup tournament.
-
E.
sharedMostWorldCupTitlesWith
Indicates that two entities hold the same highest number of World Cup titles compared to all other entities.
- 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_69ab4958e76481908a235377dd921c9e |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd65d6a988190aaaac8e98540a14f |
completed | March 7, 2026, 7:40 a.m. |
| PD | Predicate disambiguation | batch_69abd0bd996c8190ba8b9d6e4333b8d4 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:46 p.m.