Triple
T107798
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Summer Olympics 1900 |
E2177
|
entity |
| Predicate | numberOfFemaleAthletes |
P7896
|
FINISHED |
| Object | 22 |
—
|
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: 22 | Statement: [Summer Olympics 1900, numberOfFemaleAthletes, 22]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFemaleAthletes Context triple: [Summer Olympics 1900, numberOfFemaleAthletes, 22]
-
A.
numberOfSports
Indicates the quantity of distinct sports associated with or involved in a given entity.
-
B.
hasFemaleEquivalent
Indicates that one entity serves as the female counterpart or equivalent of another entity.
-
C.
hasNumberOfStudentAthletes
Indicates the relationship that specifies how many student athletes are associated with a given entity.
-
D.
hasAthletics
Indicates that an entity participates in, is associated with, or offers athletics-related activities or programs.
-
E.
studentAthletes
Indicates a relationship where the entities are athletes who are also enrolled as students, combining academic and athletic roles.
- F. None of above. chosen
Provenance (4 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_69a24fcdaeb48190a2d796677e4b3281 |
completed | Feb. 28, 2026, 2:15 a.m. |
| NER | Named-entity recognition | batch_69a25a1199ac8190ac65ffaaf45b4f5b |
completed | Feb. 28, 2026, 2:59 a.m. |
| PD | Predicate disambiguation | batch_69a2563e7188819091e9a94e071991d7 |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a25a10d6448190bee47847d5c13b84 |
completed | Feb. 28, 2026, 2:59 a.m. |
Created at: Feb. 28, 2026, 2:20 a.m.