Triple
T5869644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 3x3 basketball |
E130481
|
entity |
| Predicate | numberOfOfficials |
P67611
|
FINISHED |
| Object | 2 referees on court |
—
|
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: 2 referees on court | Statement: [3x3 basketball, numberOfOfficials, 2 referees on court]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfOfficials Context triple: [3x3 basketball, numberOfOfficials, 2 referees on court]
-
A.
numberOfCommissioners
Indicates the specific count of commissioners associated with a given entity or context.
-
B.
numberOfJudges
Indicates the total count of judges associated with a particular case, event, or entity.
-
C.
officersAre
Indicates that certain individuals hold the role or position of officers within a specified group, organization, or context.
-
D.
numberOfExecutives
Indicates the total count of executives associated with a given entity or context.
-
E.
numberOfAppointedMembers
Indicates the specific count of members who have been formally appointed to a group, body, or position.
- F. None of above. chosen
Provenance (4 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. |
| PDg | Predicate description generation | batch_69c044fe17d08190b9bf47b13863ef52 |
completed | March 22, 2026, 7:37 p.m. |
Created at: March 22, 2026, 3:56 p.m.