Triple
T10628936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bouzeron |
E250397
|
entity |
| Predicate | wineClassificationLevel |
P12905
|
FINISHED |
| Object | village-level AOC |
—
|
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: village-level AOC | Statement: [Bouzeron, wineClassificationLevel, village-level AOC]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineClassificationLevel Context triple: [Bouzeron, wineClassificationLevel, village-level AOC]
-
A.
wineQualityLevelProduced
Indicates the quality level or grade assigned to the wine that is produced in the described production event or process.
-
B.
wineClassificationSystem
chosen
Indicates a system or scheme used to categorize and organize wines based on defined criteria such as origin, style, or quality.
-
C.
wineCategory
Indicates the classification or type of wine that an entity (such as a specific wine) belongs to.
-
D.
wineStyle
Indicates the stylistic category or type of wine (such as its production style, sweetness, body, or other defining characteristics) associated with an entity.
-
E.
wineStructure
Indicates the overall sensory framework of a wine, encompassing how its components like acidity, tannin, body, and alcohol are balanced and interact.
- 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_69d6aa5993448190a493b790b8f85010 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6df92f8388190a8bcff96809d8eb4 |
completed | April 8, 2026, 11:06 p.m. |
| PD | Predicate disambiguation | batch_69d6dd7fae088190973f70c69738af49 |
completed | April 8, 2026, 10:58 p.m. |
Created at: April 8, 2026, 8:59 p.m.