Triple
T8689917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dão wine region |
E206259
|
entity |
| Predicate | importantWhiteGrape |
P28269
|
FINISHED |
| Object | Bical |
—
|
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: Bical | Statement: [Dão wine region, importantWhiteGrape, Bical]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: importantWhiteGrape Context triple: [Dão wine region, importantWhiteGrape, Bical]
-
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
chosen
Indicates that a grape variety is traditionally or historically used in a specific region, wine style, or cultural winemaking practice.
-
C.
primaryGrapeUse
Indicates that a grape variety is primarily used for a particular purpose, such as winemaking, table consumption, or raisin production.
-
D.
secondaryGrape
Indicates that one grape variety serves as a secondary or supporting component in a wine blend relative to the primary grape.
-
E.
whiteWineShare
Indicates the proportion or share of white wine within a larger set, such as total wine consumption, production, or sales.
- 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_69ca835481fc819084e33d3bc883bfa6 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5734602c81909a0687e00f4a4a26 |
completed | March 31, 2026, 11:22 p.m. |
| PD | Predicate disambiguation | batch_69cc4569f9048190b9c86b4c81103d35 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:33 p.m.