Triple
T8380764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harry Dénis |
E197680
|
entity |
| Predicate | hasSportNumberOfEvents |
P2438
|
FINISHED |
| Object | three Olympic Games (1920, 1924, 1928) |
—
|
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: three Olympic Games (1920, 1924, 1928) | Statement: [Harry Dénis, hasSportNumberOfEvents, three Olympic Games (1920, 1924, 1928)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSportNumberOfEvents Context triple: [Harry Dénis, hasSportNumberOfEvents, three Olympic Games (1920, 1924, 1928)]
-
A.
numberOfSports
Indicates the quantity of distinct sports associated with or involved in a given entity.
-
B.
hasSportingEvent
Indicates that a sporting event is associated with, held at, or organized by a given entity.
-
C.
sportsCount
Indicates the number of sports associated with or involved in a given entity or context.
-
D.
sportNumber
Indicates the specific jersey or uniform number associated with an athlete in a sporting context.
-
E.
numberOfEvents
chosen
Indicates the quantity or count of events associated with a given entity or context.
- 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_69ca82f64c188190af4e1608036b865d |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb80c57080819097eef2b7e46eaaee |
completed | March 31, 2026, 8:07 a.m. |
| PD | Predicate disambiguation | batch_69cb70cfe82881909fe374ba52649e84 |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 6:02 p.m.