Triple
T2708383
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Auxey-Duresses |
E59797
|
entity |
| Predicate | wineMarketPosition |
P23126
|
FINISHED |
| Object | lesser-known Côte de Beaune appellation |
—
|
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: lesser-known Côte de Beaune appellation | Statement: [Auxey-Duresses, wineMarketPosition, lesser-known Côte de Beaune appellation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineMarketPosition Context triple: [Auxey-Duresses, wineMarketPosition, lesser-known Côte de Beaune appellation]
-
A.
wineReputation
chosen
Indicates the perceived quality, prestige, or standing of a wine based on expert opinion, consumer perception, or historical recognition.
-
B.
wineLaw
Indicates a legal or regulatory relationship governing the production, sale, labeling, or distribution of wine.
-
C.
wineRegionStatus
Indicates the official classification or recognition status assigned to a wine-producing region.
-
D.
wineStructure
Indicates the overall sensory framework of a wine, encompassing how its components like acidity, tannin, body, and alcohol are balanced and interact.
-
E.
wineExport
Indicates a relationship where one entity exports wine to another entity or destination.
- 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_69ab4ac92a088190bc74bca14038e3de |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abda73de1c81908f5d6b0383e23144 |
completed | March 7, 2026, 7:57 a.m. |
| PD | Predicate disambiguation | batch_69abd8224c688190bb4a362360b03007 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:55 p.m.