Triple
T8698846
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cynar VA |
E206470
|
entity |
| Predicate | hasRiderProfession |
P35550
|
FINISHED |
| Object | professional show jumper |
—
|
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: professional show jumper | Statement: [Cynar VA, hasRiderProfession, professional show jumper]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRiderProfession Context triple: [Cynar VA, hasRiderProfession, professional show jumper]
-
A.
isAssociatedWithProfessionOfBearer
Indicates that one entity is connected to, or involved with, the profession or occupational role held by another entity.
-
B.
memberProfession
chosen
Indicates that a member or individual holds or practices a particular profession or occupation.
-
C.
notableRiderType
Indicates that an entity is notably associated with a particular type or category of rider (e.g., cyclist, jockey, driver).
-
D.
includesProfession
Indicates that one entity’s set of attributes, roles, or members contains a specific profession as part of it.
-
E.
hasProfessionalStatus
Indicates that an entity holds a particular professional standing, rank, or qualification within a field or occupation.
- 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_69ca83555b6c8190abe930dd397e863b |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc58b1434081908f50480bfb6f9d90 |
completed | March 31, 2026, 11:28 p.m. |
| PD | Predicate disambiguation | batch_69cc456bda508190a9aa0fb92760739e |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:34 p.m.