Triple
T3157128
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | short track speed skating at the 1994 Winter Olympics |
E66010
|
entity |
| Predicate | numberOfWomenEvents |
P2438
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [short track speed skating at the 1994 Winter Olympics, numberOfWomenEvents, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfWomenEvents Context triple: [short track speed skating at the 1994 Winter Olympics, numberOfWomenEvents, 5]
-
A.
numberOfFemaleAthletes
Indicates the count of athletes who are female in a given context or group.
-
B.
numberOfEvents
chosen
Indicates the quantity or count of events associated with a given entity or context.
-
C.
numberOfSports
Indicates the quantity of distinct sports associated with or involved in a given entity.
-
D.
numberOfMaleAthletes
Indicates the quantity of athletes in a given group or context who are male.
-
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_69ad85850c1481908a9e9c6242238de2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada5eafa4c8190a65cc1312823144c |
completed | March 8, 2026, 4:38 p.m. |
| PD | Predicate disambiguation | batch_69ad9dfbf0348190952a6bca8fc5fed1 |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:05 p.m.