Triple
T17333658
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Limoux |
E420878
|
entity |
| Predicate | regionWineStyle |
P105586
|
FINISHED |
| Object | sparkling white wine |
—
|
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: sparkling white wine | Statement: [Limoux, regionWineStyle, sparkling white wine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionWineStyle Context triple: [Limoux, regionWineStyle, sparkling white wine]
-
A.
wineRegionCategory
Indicates a classification relationship where a wine region is assigned to a specific category or type of wine-producing area.
-
B.
wineClassificationRegion
Indicates that a wine is classified according to the geographic region where it is produced or designated.
-
C.
wineRegion
Indicates the geographical region or area where a particular wine is produced or originates.
-
D.
wineSubregion
Indicates that one region is a subregion within a larger, defined wine-producing region.
-
E.
wineStyleOrigin
chosen
Indicates that a particular wine style originated in or is traditionally associated with a specific geographic region or place.
- 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_69d889d3adc881909319f1edb8d2a956 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a106df48190a50f96febc13cde7 |
completed | April 19, 2026, 2:12 a.m. |
| PD | Predicate disambiguation | batch_69e3b021a5bc81909ae55406f9d0b37f |
completed | April 18, 2026, 4:24 p.m. |
Created at: April 10, 2026, 5:43 a.m.