Triple
T31698205
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | speed skating at the 2006 Winter Olympics |
E808974
|
entity |
| Predicate | includedGender |
P203335
|
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: [speed skating at the 2006 Winter Olympics, includedGender, men]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includedGender Context triple: [speed skating at the 2006 Winter Olympics, includedGender, men]
-
A.
includesBothGenders
Indicates that the referenced group, set, or category contains members of both male and female genders.
-
B.
genderCategoryIncludes
Indicates that a given gender category encompasses or contains the specified gender identity or subgroup.
-
C.
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.
-
D.
genderSpecificity
Indicates whether the relationship or action applies specifically to a particular gender or is gender-neutral.
-
E.
alsoIncludesGenderOfDivers
Indicates that the referenced set or category additionally encompasses the gender identity of divers.
- 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_69f348de914081909fc8edff56f34dbe |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a016336c58081909c58c5772e6fb488 |
completed | May 11, 2026, 5:03 a.m. |
| PD | Predicate disambiguation | batch_6a0160f25d8081909a6aaa375e9850b0 |
completed | May 11, 2026, 4:54 a.m. |
| PDg | Predicate description generation | batch_6a0163359e588190ba365f65b2d86435 |
completed | May 11, 2026, 5:03 a.m. |
Created at: April 30, 2026, 11:11 p.m.