Triple
T15998893
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1994 Olympic ladies' singles free skate |
E388045
|
entity |
| Predicate | competitionSegmentScoreType |
P48244
|
FINISHED |
| Object | technical merit |
—
|
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: technical merit | Statement: [1994 Olympic ladies' singles free skate, competitionSegmentScoreType, technical merit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: competitionSegmentScoreType Context triple: [1994 Olympic ladies' singles free skate, competitionSegmentScoreType, technical merit]
-
A.
scoringType
chosen
Indicates the method or criteria by which performance, outcomes, or results are evaluated and assigned a score in a given context.
-
B.
typeOfCompetition
Indicates the specific kind or category of competition in which an entity participates or is involved.
-
C.
decisiveScoreType
Indicates the type or category of score used to determine a decisive outcome in a comparison or decision process.
-
D.
scoringUnit
Indicates that one entity functions as a unit or component responsible for scoring or assigning scores to another entity.
-
E.
individualScoring
Indicates that a specific individual receives or is assigned a particular score or evaluation in a given 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_69d86daa562c81908aacc179c0fe8fb5 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142dc081c819082527e3fa8773460 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:55 a.m.