Triple
T23407954
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luge at the 2018 Winter Olympics |
E559985
|
entity |
| Predicate | hasMixedGenderEvent |
P136236
|
FINISHED |
| Object | team relay |
—
|
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: team relay | Statement: [Luge at the 2018 Winter Olympics, hasMixedGenderEvent, team relay]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMixedGenderEvent Context triple: [Luge at the 2018 Winter Olympics, hasMixedGenderEvent, team relay]
-
A.
hasHostGender
Indicates that an entity has or is associated with a specific gender of its host.
-
B.
hasPerformerGenderComposition
Indicates the gender makeup of the group of performers involved in an event or performance.
-
C.
hasFemaleCompetitors
Indicates that an entity participates in a competitive context where at least some of the competitors are female.
-
D.
includesBothGenders
chosen
Indicates that the referenced group, set, or category contains members of both male and female genders.
-
E.
includesMixedSexRace
Indicates that the group or context involves individuals of more than one sex and more than one race.
- F. None of above.
Provenance (3 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_69e2454b3a5881909c64773dc8a5d289 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1a50f3f90819084fb682597fee1e1 |
completed | April 29, 2026, 6:28 a.m. |
| PD | Predicate disambiguation | batch_69f061ed34288190a2e5e8cae03b0095 |
completed | April 28, 2026, 7:29 a.m. |
Created at: April 17, 2026, 5:38 p.m.