Triple
T3853636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maury AOC |
E85357
|
entity |
| Predicate | primaryWineType |
P2082
|
FINISHED |
| Object | fortified sweet red 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: fortified sweet red wine | Statement: [Maury AOC, primaryWineType, fortified sweet red wine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryWineType Context triple: [Maury AOC, primaryWineType, fortified sweet red wine]
-
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.
wineStyle
chosen
Indicates the stylistic category or type of wine (such as its production style, sweetness, body, or other defining characteristics) associated with an entity.
-
C.
wineCategory
Indicates the classification or type of wine that an entity (such as a specific wine) belongs to.
-
D.
wineClassificationSystem
Indicates a system or scheme used to categorize and organize wines based on defined criteria such as origin, style, or quality.
-
E.
traditionalGrapeVariety
Indicates that a grape variety is traditionally or historically used in a specific region, wine style, or cultural winemaking practice.
- 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_69aed936de1c81908f91bed80f70abb2 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeec0438308190865ff74bee5a1cf2 |
completed | March 9, 2026, 3:49 p.m. |
| PD | Predicate disambiguation | batch_69aee750377c8190af70c79768c0edd8 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:19 p.m.