Triple
T5869590
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FIBA 3x3 World Cup |
E130480
|
entity |
| Predicate | numberOfTeamsPerGenderApprox |
P18041
|
FINISHED |
| Object | 20 to 24 teams |
—
|
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: 20 to 24 teams | Statement: [FIBA 3x3 World Cup, numberOfTeamsPerGenderApprox, 20 to 24 teams]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTeamsPerGenderApprox Context triple: [FIBA 3x3 World Cup, numberOfTeamsPerGenderApprox, 20 to 24 teams]
-
A.
hasGenderedTeams
Indicates that the entity organizes or participates in teams that are separated or defined based on gender.
-
B.
hasGenderedTeam
Indicates that a team is organized or classified according to the gender of its members.
-
C.
hasNumberOfTeams
Indicates the quantity of teams associated with or contained by a given entity.
-
D.
teamCountType
Indicates how the number of teams is categorized or measured within a given context.
-
E.
numberOfTeamsVariesBetween
chosen
Indicates that the count of teams involved changes within a specified range or across different instances or conditions.
- 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_69c0085047dc8190af24e311edad3c07 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c044ffaef081909faaa7f420a3b9b7 |
completed | March 22, 2026, 7:37 p.m. |
| PD | Predicate disambiguation | batch_69c03347e51c81909053bcf34e3b88ab |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:56 p.m.