Triple
T15744621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | China women's national football team |
E381689
|
entity |
| Predicate | asianCupTitles |
P50371
|
FINISHED |
| Object | 9 |
—
|
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: 9 | Statement: [China women's national football team, asianCupTitles, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: asianCupTitles Context triple: [China women's national football team, asianCupTitles, 9]
-
A.
asiaCupWinner
Indicates that one entity is the champion or winning team of the Asia Cup tournament in a given edition or year.
-
B.
wonAsianCupWinnersCup
Indicates that one entity (typically a sports team or club) has won the Asian Cup Winners' Cup competition.
-
C.
asianCupAppearances
Indicates the number of times an entity has participated in the AFC Asian Cup tournament.
-
D.
AsianChampionshipTitles
chosen
Indicates the number of championship titles an entity has won in Asian-level competitions or tournaments.
-
E.
gulfCupTitles
Indicates the number of Gulf Cup championship titles an entity (typically a national football team) has won.
- 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0b4d6b5788190883746ee82c799f5 |
completed | April 16, 2026, 10:07 a.m. |
| PD | Predicate disambiguation | batch_69e0052c6208819098165d61d378d13b |
completed | April 15, 2026, 9:37 p.m. |
Created at: April 10, 2026, 4:46 a.m.