Triple
T4492221
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | B12 |
E100602
|
entity |
| Predicate | sponsorsWomenSports |
P790
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [B12, sponsorsWomenSports, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sponsorsWomenSports Context triple: [B12, sponsorsWomenSports, true]
-
A.
alsoSponsorsSport
Indicates that an entity that sponsors one sport also sponsors another sport.
-
B.
sportsSponsored
Indicates that one entity provides financial or material sponsorship to support another entity’s sports-related activities or events.
-
C.
sponsorSport
chosen
Indicates that one entity financially or materially supports a sport or sporting activity, typically in exchange for promotion or association.
-
D.
sponsors
Indicates that one entity provides financial or material support to another, often in exchange for association, promotion, or fulfillment of certain activities or goals.
-
E.
numberOfFemaleAthletes
Indicates the count of athletes who are female in a given context or group.
- 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_69bd43cdf15081909a4fa2585ff63b3e |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd556f99b48190ae60506a35b43c29 |
completed | March 20, 2026, 2:10 p.m. |
| PD | Predicate disambiguation | batch_69bd5213e3d0819094b026989e686f01 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 12:59 p.m.