Triple
T17038115
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | mixed short track speed skating relay |
E413372
|
entity |
| Predicate | hasGenderFormat |
P125610
|
FINISHED |
| Object | mixed-gender |
—
|
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: mixed-gender | Statement: [mixed short track speed skating relay, hasGenderFormat, mixed-gender]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderFormat Context triple: [mixed short track speed skating relay, hasGenderFormat, mixed-gender]
-
A.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
B.
hasGenderVariant
Indicates that one entity is a gender-specific form or variant of another entity.
-
C.
hasGenderInterpretation
Indicates that an entity is associated with a particular interpretation or understanding of gender.
-
D.
hasGenderSystem
Indicates that an entity employs or is characterized by a particular system for categorizing gender.
-
E.
hasGenderInText
Indicates that a specified gender is explicitly mentioned or assigned to an entity within a given text.
- 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_69d886cd18288190b006abab23f811b7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d8f45f84819092cfb27cc33da026 |
completed | April 18, 2026, 7:18 p.m. |
| PD | Predicate disambiguation | batch_69e35d60a588819084f53ef9f8b2e7c0 |
completed | April 18, 2026, 10:30 a.m. |
| PDg | Predicate description generation | batch_69e3753f93c88190808fec5692f66699 |
completed | April 18, 2026, 12:12 p.m. |
Created at: April 10, 2026, 5:33 a.m.