Triple
T3157133
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | short track speed skating at the 1994 Winter Olympics |
E66010
|
entity |
| Predicate | featuredGender |
P46670
|
FINISHED |
| Object | men |
—
|
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 | Statement: [short track speed skating at the 1994 Winter Olympics, featuredGender, men]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuredGender Context triple: [short track speed skating at the 1994 Winter Olympics, featuredGender, men]
-
A.
genderCategories
Indicates the classification of an entity into one or more gender-related categories or identities.
-
B.
sexOrGender
Indicates that one entity has a specified biological sex or socially constructed gender identity.
-
C.
genderRule
Indicates a rule or constraint that determines how gender-related properties or classifications should be assigned or interpreted in a given context.
-
D.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
E.
genderUsage
Indicates how a particular gender is applied, referenced, or treated within a given context or system.
- 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_69ad85850c1481908a9e9c6242238de2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada5eafa4c8190a65cc1312823144c |
completed | March 8, 2026, 4:38 p.m. |
| PD | Predicate disambiguation | batch_69ad9dfbf0348190952a6bca8fc5fed1 |
completed | March 8, 2026, 4:04 p.m. |
| PDg | Predicate description generation | batch_69ada1e4f7288190a80a1672e458132d |
completed | March 8, 2026, 4:20 p.m. |
Created at: March 8, 2026, 3:05 p.m.