Triple
T5004151
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Côte-Rôtie |
E112445
|
entity |
| Predicate | typicalViognierPercentage |
P61433
|
FINISHED |
| Object | 0–10% |
—
|
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: 0–10% | Statement: [Côte-Rôtie, typicalViognierPercentage, 0–10%]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalViognierPercentage Context triple: [Côte-Rôtie, typicalViognierPercentage, 0–10%]
-
A.
primaryGrapeVariety
Indicates that one entity is the main or predominant grape variety used in producing the other entity (typically a wine or wine-based product).
-
B.
traditionalGrapeVariety
Indicates that a grape variety is traditionally or historically used in a specific region, wine style, or cultural winemaking practice.
-
C.
viticulturalCharacteristic
Indicates a relationship where a specific trait, quality, or property is attributed to viticulture or grape-growing practices.
-
D.
wineGrapesCultivated
Indicates that certain grape varieties are grown or cultivated specifically for producing wine.
-
E.
producesWine
Indicates that one entity creates or manufactures wine as a product.
- F. None of above. chosen
Provenance (4 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_69bd4433d0b08190877e83959ef40d81 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7472a1dc8190942f568a81fdd961 |
completed | March 20, 2026, 4:23 p.m. |
| PD | Predicate disambiguation | batch_69bd714aee2481908fb0dd5fa2daf3a1 |
completed | March 20, 2026, 4:09 p.m. |
| PDg | Predicate description generation | batch_69bd74713fc88190916c2b04cd2e677e |
completed | March 20, 2026, 4:23 p.m. |
Created at: March 20, 2026, 1:35 p.m.