Triple
T17091056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | France women’s national basketball team |
E414725
|
entity |
| Predicate | hasWonWorldCupMedal |
P44482
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [France women’s national basketball team, hasWonWorldCupMedal, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWonWorldCupMedal Context triple: [France women’s national basketball team, hasWonWorldCupMedal, true]
-
A.
WorldCupMedal
Indicates that an entity has received a medal (e.g., gold, silver, bronze) for its performance in a FIFA World Cup tournament.
-
B.
wonWorldCupSilverMedal
Indicates that an entity achieved second place and received a silver medal in a World Cup competition.
-
C.
wonMedalAt
chosen
Indicates that an entity received a medal as a result of participating in a specific event or competition.
-
D.
worldCupWon
Indicates that the subject has won the FIFA World Cup tournament.
-
E.
numberOfWorldCupWins
Indicates how many times an entity has won the FIFA 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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbfa09b08190be4303dd0d174feb |
completed | April 18, 2026, 7:31 p.m. |
| PD | Predicate disambiguation | batch_69e35d67b14481909fcdbdeaa5c34785 |
completed | April 18, 2026, 10:31 a.m. |
Created at: April 10, 2026, 5:35 a.m.