Triple
T28985368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | speed skating at the 1998 Winter Olympics |
E734666
|
entity |
| Predicate | genderOfCompetitors |
P166271
|
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 1998 Winter Olympics, genderOfCompetitors, men]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderOfCompetitors Context triple: [speed skating at the 1998 Winter Olympics, genderOfCompetitors, men]
-
A.
hasFemaleCompetitors
Indicates that an entity participates in a competitive context where at least some of the competitors are female.
-
B.
genderOfMembers
Indicates the gender or genders associated with the members of a group or organization.
-
C.
genderOfMascot
Indicates the gender associated with a particular mascot.
-
D.
winnerGender
Indicates the gender of the entity that is the winner in a given event or competition.
-
E.
eligiblePlayersGender
Indicates that the relationship specifies which player genders are allowed or considered eligible in a given context.
- 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_69f05b0dd9b481908b7901e1c95ff6b2 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f66003a3f48190a2ba6da5aafbb5cb |
completed | May 2, 2026, 8:35 p.m. |
| PD | Predicate disambiguation | batch_69f65c2198208190a3954086c22cfcbf |
completed | May 2, 2026, 8:18 p.m. |
| PDg | Predicate description generation | batch_69f65f75ac608190a62cd6afce14f68e |
completed | May 2, 2026, 8:32 p.m. |
Created at: April 28, 2026, 9:14 a.m.