Triple
T2528433
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Savigny-lès-Beaune |
E56094
|
entity |
| Predicate | locatedInWineSubregion |
P8842
|
FINISHED |
| Object | Côte de Beaune vineyards |
—
|
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: Côte de Beaune vineyards | Statement: [Savigny-lès-Beaune, locatedInWineSubregion, Côte de Beaune vineyards]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locatedInWineSubregion Context triple: [Savigny-lès-Beaune, locatedInWineSubregion, Côte de Beaune vineyards]
-
A.
wineRegion
Indicates the geographical region or area where a particular wine is produced or originates.
-
B.
wineRegionPartOf
chosen
Indicates that a wine-producing region is geographically or administratively contained within a larger wine region or area.
-
C.
nearWineRegion
Indicates that one entity is located close to or in the vicinity of a wine-producing region.
-
D.
wineGrapesCultivated
Indicates that certain grape varieties are grown or cultivated specifically for producing wine.
-
E.
viticulturalAreaDesignation
Indicates that a specific geographic area is officially designated or recognized for viticulture (grape growing and wine production).
- 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_69ab4a48e4f081908f1218d244608659 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd257ea908190a010c0b785853546 |
completed | March 7, 2026, 7:23 a.m. |
| PD | Predicate disambiguation | batch_69abd0c2e34c8190a914d5c2afba147c |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:46 p.m.