Triple
T36147375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FEI Vaulting |
E1045487
|
entity |
| Predicate | scoredOn |
P184751
|
FINISHED |
| Object | technique |
—
|
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: technique | Statement: [FEI Vaulting, scoredOn, technique]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: scoredOn Context triple: [FEI Vaulting, scoredOn, technique]
-
A.
scoredFor
Indicates that one entity achieved points or a score on behalf of another entity, such as a player scoring for a team.
-
B.
scoredBy
Indicates that a particular score, point, or result was achieved or obtained by a specific entity.
-
C.
scoredAsPartOf
Indicates that an entity achieved a score or rating specifically within the context of being part of a larger group, event, or composite activity.
-
D.
hasScoredFor
Indicates that one entity has scored points, goals, or similar achievements on behalf of another entity, such as a team, organization, or side.
-
E.
alsoScoredFor
Indicates that an individual who scored for one team or entity has also scored for another team or entity.
- 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_69f76e37ace88190a906b107d388f5d1 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7b3e2f3c08190be4fd1ae4fa1266d |
completed | May 3, 2026, 8:45 p.m. |
| PD | Predicate disambiguation | batch_69f7b1bcc47081909fe7d592ac69006c |
completed | May 3, 2026, 8:36 p.m. |
| PDg | Predicate description generation | batch_69f7b3e0f1c88190985feab6cee8b05e |
completed | May 3, 2026, 8:45 p.m. |
Created at: May 3, 2026, 4:08 p.m.