Triple
T6002237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | China women's national basketball team |
E133623
|
entity |
| Predicate | AsiaCupTitles |
P50371
|
FINISHED |
| Object | multiple |
—
|
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: multiple | Statement: [China women's national basketball team, AsiaCupTitles, multiple]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: AsiaCupTitles Context triple: [China women's national basketball team, AsiaCupTitles, multiple]
-
A.
asiaCupWinner
Indicates that one entity is the champion or winning team of the Asia Cup tournament in a given edition or year.
-
B.
AsianChampionshipTitles
chosen
Indicates the number of championship titles an entity has won in Asian-level competitions or tournaments.
-
C.
asiaCupBestPerformance
Indicates the best result or highest achievement an entity has attained in the Asia Cup competition.
-
D.
conferenceChampionTrophy
Indicates that an entity has won and been awarded the championship trophy for a specific conference.
-
E.
afconTitles
Indicates the number of Africa Cup of Nations (AFCON) championship titles an entity 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_69c00872444c8190bfaf1739dcec765c |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04ee9cb0c8190a3361c36ac3944af |
completed | March 22, 2026, 8:19 p.m. |
| PD | Predicate disambiguation | batch_69c049e3316c819087ea635fa7ee8472 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:05 p.m.