Triple
T17174674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NCAA Men's and Women's Fencing Committee |
E416827
|
entity |
| Predicate | overseesGenderCategory |
P126614
|
FINISHED |
| Object | men's fencing |
—
|
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 fencing | Statement: [NCAA Men's and Women's Fencing Committee, overseesGenderCategory, men's fencing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: overseesGenderCategory Context triple: [NCAA Men's and Women's Fencing Committee, overseesGenderCategory, men's fencing]
-
A.
genderCategories
Indicates the classification of an entity into one or more gender-related categories or identities.
-
B.
governsGender
Indicates that one entity determines or constrains the gender classification or gender-related properties of another entity.
-
C.
isGenderSpecificCategory
Indicates that the category applies specifically to one gender rather than being gender-neutral.
-
D.
genderSpecificity
Indicates whether the relationship or action applies specifically to a particular gender or is gender-neutral.
-
E.
hasGenderSystem
Indicates that an entity employs or is characterized by a particular system for categorizing gender.
- 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_69d886d5f34c8190b24564dfaa63f3fb |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3fc0c329081909f118bd4b7be8653 |
completed | April 18, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69e383141ae0819096acd71683637cbc |
completed | April 18, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69e39c2fedb881908bfed2c3e5f2616a |
completed | April 18, 2026, 2:58 p.m. |
Created at: April 10, 2026, 5:37 a.m.