Triple
T27907900
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vallée de la Marne wine region |
E705838
|
entity |
| Predicate | alsoGrowsGrape |
P152021
|
FINISHED |
| Object | Pinot Noir |
—
|
NE NERFINISHED |
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 Noir | Statement: [Vallée de la Marne wine region, alsoGrowsGrape, Pinot Noir]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: alsoGrowsGrape Context triple: [Vallée de la Marne wine region, alsoGrowsGrape, Pinot Noir]
-
A.
alsoUsesGrapeVariety
chosen
Indicates that one entity, in addition to another, makes use of the same grape variety in its composition or production.
-
B.
grapeFamily
Indicates that one entity belongs to, or is classified within, the same botanical family as grapes.
-
C.
producesWine
Indicates that one entity creates or manufactures wine as a product.
-
D.
grapesVinifiedAt
Indicates that grapes were processed and turned into wine at a specific location or facility.
-
E.
alsoProducesWineIn
Indicates that the subject, in addition to other products or activities, produces wine in the specified location or 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_69ef96b5aad08190be36a277c31e7004 |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_69f63b317e048190963989b732b25b91 |
completed | May 2, 2026, 5:58 p.m. |
| PD | Predicate disambiguation | batch_69f6370ea79c81909b761821ee0fa698 |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 27, 2026, 6:47 p.m.