Triple
T11727984
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hoya Hoop Club |
E278820
|
entity |
| Predicate | supportsGender |
P29732
|
FINISHED |
| Object | men's basketball |
—
|
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 basketball | Statement: [Hoya Hoop Club, supportsGender, men's basketball]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsGender Context triple: [Hoya Hoop Club, supportsGender, men's basketball]
-
A.
hasGenderNeutrality
Indicates that something (such as a term, form, or expression) is neutral with respect to gender and does not specify or imply any particular gender.
-
B.
usedByGender
chosen
Indicates that something is utilized, applied, or engaged in by entities of a specified gender.
-
C.
hasGenderVariant
Indicates that one entity is a gender-specific form or variant of another entity.
-
D.
genderTarget
Indicates that an action, message, or effect is specifically directed toward entities of a particular gender.
-
E.
hasGenderInterpretation
Indicates that an entity is associated with a particular interpretation or understanding of gender.
- 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_69d6aaffec6881908bead509e8621742 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a4d80ef881908cab956787ab07fd |
completed | April 10, 2026, 7:20 a.m. |
| PD | Predicate disambiguation | batch_69d88a7f51248190bf492bd7509b5413 |
completed | April 10, 2026, 5:28 a.m. |
Created at: April 8, 2026, 9:41 p.m.