Triple
T9015916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cal Poly Mustangs |
E215592
|
entity |
| Predicate | sponsoredSportsCountApprox |
P2437
|
FINISHED |
| Object | 21 |
—
|
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: 21 | Statement: [Cal Poly Mustangs, sponsoredSportsCountApprox, 21]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sponsoredSportsCountApprox Context triple: [Cal Poly Mustangs, sponsoredSportsCountApprox, 21]
-
A.
sportsSponsored
Indicates that one entity provides financial or material sponsorship to support another entity’s sports-related activities or events.
-
B.
sponsorSport
Indicates that one entity financially or materially supports a sport or sporting activity, typically in exchange for promotion or association.
-
C.
alsoSponsorsSport
Indicates that an entity that sponsors one sport also sponsors another sport.
-
D.
sportsCount
Indicates the number of sports associated with or involved in a given entity or context.
-
E.
numberOfSports
chosen
Indicates the quantity of distinct sports associated with or involved in a given 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_69ca83a38aa88190bf1bb80c4548b5e2 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc69fc0e4c819080b60456375f94cd |
completed | April 1, 2026, 12:42 a.m. |
| PD | Predicate disambiguation | batch_69cc5edf84408190aa5f57cb8bfd00e1 |
completed | March 31, 2026, 11:55 p.m. |
Created at: March 30, 2026, 7:06 p.m.