Triple
T17763541
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2020 Winter Youth Olympics |
E443442
|
entity |
| Predicate | disciplinesCount |
P128843
|
FINISHED |
| Object | 16 |
—
|
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: 16 | Statement: [2020 Winter Youth Olympics, disciplinesCount, 16]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: disciplinesCount Context triple: [2020 Winter Youth Olympics, disciplinesCount, 16]
-
A.
disciplineCount
Indicates the number of disciplinary actions or incidents associated with an entity.
-
B.
numberOfQualifiersPerDiscipline
Indicates the count of qualifier instances associated with each specific discipline.
-
C.
typeDiscipline
Indicates that an entity is associated with, categorized under, or characterized by a particular discipline or field of study.
-
D.
subjectCount
Indicates the number of subjects associated with or involved in a given entity or context.
-
E.
featuredDiscipline
Indicates that one discipline is highlighted or given special prominence in relation to another entity or context.
- 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_69d8b9edf16c8190a59ebd245d378f4f |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e485f9fa188190aef5ffeb0b628632 |
completed | April 19, 2026, 7:36 a.m. |
| PD | Predicate disambiguation | batch_69e3d8d8e538819084f1584426b41d5e |
completed | April 18, 2026, 7:17 p.m. |
| PDg | Predicate description generation | batch_69e3db7704588190a34a422421152173 |
completed | April 18, 2026, 7:28 p.m. |
Created at: April 10, 2026, 10:11 a.m.