Triple
T28870324
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bucknell University Bison athletics program |
E732123
|
entity |
| Predicate | sponsorOfGender |
P167934
|
FINISHED |
| Object | men's varsity sports |
—
|
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: men's varsity sports | Statement: [Bucknell University Bison athletics program, sponsorOfGender, men's varsity sports]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sponsorOfGender Context triple: [Bucknell University Bison athletics program, sponsorOfGender, men's varsity sports]
-
A.
sponsoredGender
Indicates that one entity provides financial or material sponsorship specifically related to the gender of another entity.
-
B.
genderOfMascot
Indicates the gender associated with a particular mascot.
-
C.
winnerGender
Indicates the gender of the entity that is the winner in a given event or competition.
-
D.
plugGender
Indicates that one entity’s connector has a specified gender (e.g., male, female, neutral) in relation to another connector or interface.
-
E.
bearerGender
Indicates the gender associated with the bearer in the relationship or context.
- 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_69f05b06807c81909b4bbd4c20403a2b |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f66e5f7e30819094530abceabd5f43 |
completed | May 2, 2026, 9:36 p.m. |
| PD | Predicate disambiguation | batch_69f66abfdaf08190a55f14c70be6fd4d |
completed | May 2, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69f66d75a8788190aa9ca2c977429045 |
completed | May 2, 2026, 9:32 p.m. |
Created at: April 28, 2026, 7:31 a.m.