Triple
T29374620
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vidigueira DOC |
E744956
|
entity |
| Predicate | grapeColorAllowed |
P37246
|
FINISHED |
| Object | white grapes |
—
|
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: white grapes | Statement: [Vidigueira DOC, grapeColorAllowed, white grapes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grapeColorAllowed Context triple: [Vidigueira DOC, grapeColorAllowed, white grapes]
-
A.
grapeColorProduced
Indicates the color that is produced by or characteristic of a given grape.
-
B.
grapeVarietyAllowed
chosen
Indicates that a specific grape variety is permitted or authorized for use in a given context, such as a wine, region, or product specification.
-
C.
grapeColorForReds
Indicates that the predicate specifies the typical color of grapes used to produce red wines.
-
D.
grapeColorDistribution
Indicates how colors are proportionally or categorically distributed among a set of grapes.
-
E.
grapeColorForRosé
Indicates that a particular grape color is used in the production of rosé wine.
- 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_69f0a79ba954819094597628112c6091 |
completed | April 28, 2026, 12:27 p.m. |
| NER | Named-entity recognition | batch_69f669aca96081909c9b2aab89003f16 |
completed | May 2, 2026, 9:16 p.m. |
| PD | Predicate disambiguation | batch_69f660f4f7a88190b93c60d76b86c912 |
completed | May 2, 2026, 8:39 p.m. |
Created at: April 28, 2026, 2:30 p.m.