Triple
T16656555
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VII Olympic Winter Games |
E404745
|
entity |
| Predicate | maleAthleteCount |
P7895
|
FINISHED |
| Object | 687 |
—
|
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: 687 | Statement: [VII Olympic Winter Games, maleAthleteCount, 687]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maleAthleteCount Context triple: [VII Olympic Winter Games, maleAthleteCount, 687]
-
A.
numberOfMaleAthletes
chosen
Indicates the quantity of athletes in a given group or context who are male.
-
B.
numberOfFemaleAthletes
Indicates the count of athletes who are female in a given context or group.
-
C.
notableCompetitorMen
Indicates that there is a notable male competitor associated with the subject in a competitive context.
-
D.
hasFemaleCompetitors
Indicates that an entity participates in a competitive context where at least some of the competitors are female.
-
E.
sportGender
Indicates that a sport or sporting event is associated with a particular gender category (e.g., men's, women's, mixed).
- 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_69d8838b5fbc81908c6575c132b82e80 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e37bfb2b308190bf3559df9fbb126f |
completed | April 18, 2026, 12:41 p.m. |
| PD | Predicate disambiguation | batch_69e319b1d7f08190b5ecb4a68c636c15 |
completed | April 18, 2026, 5:42 a.m. |
Created at: April 10, 2026, 5:18 a.m.