Triple
T34959740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint-Joseph AOC |
E1008218
|
entity |
| Predicate | whiteWineComposition |
P182136
|
FINISHED |
| Object | Marsanne and Roussanne blends or varietals |
—
|
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: Marsanne and Roussanne blends or varietals | Statement: [Saint-Joseph AOC, whiteWineComposition, Marsanne and Roussanne blends or varietals]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: whiteWineComposition Context triple: [Saint-Joseph AOC, whiteWineComposition, Marsanne and Roussanne blends or varietals]
-
A.
whiteWineFrom
Indicates that one entity is a white wine that originates from, or is produced in, the region or source specified by the other entity.
-
B.
whiteWineShare
Indicates the proportion or share of white wine within a larger set, such as total wine consumption, production, or sales.
-
C.
sparklingWineSweetnessRange
Indicates the range of sweetness levels that a sparkling wine can have or is classified within.
-
D.
wineAcidityType
Indicates the type or category of acidity associated with a given wine.
-
E.
whiteWineProductionAllowed
Indicates that producing white wine is permitted under the relevant rules, regulations, or conditions.
- 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_69f76dc69564819099e9e78aed6ff0a6 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78710282c81909146dc0be91e983f |
completed | May 3, 2026, 5:34 p.m. |
| PD | Predicate disambiguation | batch_69f784162134819098413482ef52042f |
completed | May 3, 2026, 5:21 p.m. |
| PDg | Predicate description generation | batch_69f7870dfe108190996c0c68630edc7f |
completed | May 3, 2026, 5:34 p.m. |
Created at: May 3, 2026, 4 p.m.