Triple
T36130341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chavignol |
E1044996
|
entity |
| Predicate | hasNearbyVineyardsProducing |
P124617
|
FINISHED |
| Object | Sancerre AOC wine |
—
|
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: Sancerre AOC wine | Statement: [Chavignol, hasNearbyVineyardsProducing, Sancerre AOC wine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyVineyardsProducing Context triple: [Chavignol, hasNearbyVineyardsProducing, Sancerre AOC wine]
-
A.
hasVineyardsNear
chosen
Indicates that one entity possesses or is associated with vineyards located in close geographic proximity to another entity.
-
B.
hasVineyards
Indicates that one entity possesses, contains, or is associated with vineyards used for growing grapevines.
-
C.
hasNearbyWinery
Indicates that one entity is located close to, or in the vicinity of, a winery.
-
D.
hasWinemakingFacility
Indicates that an entity possesses or is associated with a facility where winemaking activities are carried out.
-
E.
producesWine
Indicates that one entity creates or manufactures wine as a product.
- 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_69f76e356c908190abc6ca1e6a05b011 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a004b6ad4248190b402a0d01b0ebf83 |
completed | May 10, 2026, 9:10 a.m. |
| PD | Predicate disambiguation | batch_6a004ae736b881908a0efed8f63f982e |
completed | May 10, 2026, 9:07 a.m. |
Created at: May 3, 2026, 4:08 p.m.