Triple
T244741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pinot Noir |
E5011
|
entity |
| Predicate | relatedVariety |
P37
|
FINISHED |
| Object | Pinot Gris |
—
|
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: Pinot Gris | Statement: [Pinot Noir, relatedVariety, Pinot Gris]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedVariety Context triple: [Pinot Noir, relatedVariety, Pinot Gris]
-
A.
relatedTo
chosen
Indicates a general, non-specific relationship or association exists between two entities.
-
B.
parentVariety
Indicates that one variety is the direct parent or source variety from which another variety is derived or developed.
-
C.
typicalVariety
Indicates that one entity is a representative or characteristic example of the variety or type defined by another entity.
-
D.
moreDistantlyRelatedTo
Indicates that one entity is related to another by a more distant or indirect relationship compared to some closer reference relationship.
-
E.
relatedField
Indicates that one field, topic, or area of study is connected or relevant to another in subject matter or application.
- 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_69a257c3d0708190b0871c4269d273e6 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25dcd2b208190855d5d8d70a3acfc |
completed | Feb. 28, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69a25b62839c8190824064fe5da6a92a |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.