Triple
T22779874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huangpu Stadium |
E563804
|
entity |
| Predicate | inauguralTournamentHosted |
P27777
|
FINISHED |
| Object | first FIFA Women's World Cup |
—
|
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: first FIFA Women's World Cup | Statement: [Huangpu Stadium, inauguralTournamentHosted, first FIFA Women's World Cup]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inauguralTournamentHosted Context triple: [Huangpu Stadium, inauguralTournamentHosted, first FIFA Women's World Cup]
-
A.
firstTournamentHost
chosen
Indicates that the subject entity is the first host of a particular tournament or competition.
-
B.
notableTournamentHosted
Indicates that an entity has served as the host or venue for a tournament considered notable or significant.
-
C.
inauguralChampionshipMatch
Indicates the relationship in which a match is the first-ever championship contest held for a particular competition or title.
-
D.
inauguralHostCity
Indicates the city that first hosted a particular event, competition, or series.
-
E.
tournamentHost
Indicates that one entity serves as the organizer or official host of a tournament involving the other 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_69e24554497c819080b996e071de27c2 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17b63d348819085668e3d9ac78ffa |
completed | April 29, 2026, 3:30 a.m. |
| PD | Predicate disambiguation | batch_69eed2c32e8c8190b73bb9965ed47d64 |
completed | April 27, 2026, 3:06 a.m. |
Created at: April 17, 2026, 3:28 p.m.