Triple
T9355974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States at the 2002 Winter Olympics |
E225140
|
entity |
| Predicate | numberOfMaleCompetitors |
P7895
|
FINISHED |
| Object | 114 |
—
|
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: 114 | Statement: [United States at the 2002 Winter Olympics, numberOfMaleCompetitors, 114]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMaleCompetitors Context triple: [United States at the 2002 Winter Olympics, numberOfMaleCompetitors, 114]
-
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.
numberOfParticipants
Indicates the total count of entities involved in a particular event, activity, or relationship.
-
D.
typicalNumberOfAthletes
Indicates the usual or average number of athletes associated with or participating in a given context, event, or entity.
-
E.
winnerGender
Indicates the gender of the entity that is the winner in a given event or competition.
- 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_69ca842abfd48190949d71c3b86eeba8 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd4fee9d4c8190a7d121c9487ccca2 |
completed | April 1, 2026, 5:03 p.m. |
| PD | Predicate disambiguation | batch_69cc7a68ab9481909f97cb70764697cc |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:42 p.m.