Triple
T16290631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lledoner Pelut |
E395510
|
entity |
| Predicate | typicalUseInWine |
P45198
|
FINISHED |
| Object | blending grape |
—
|
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: blending grape | Statement: [Lledoner Pelut, typicalUseInWine, blending grape]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalUseInWine Context triple: [Lledoner Pelut, typicalUseInWine, blending grape]
-
A.
typicalUseInChampagne
Indicates that something is commonly or characteristically used in the production, serving, or enjoyment of champagne.
-
B.
usesWineType
Indicates that one entity makes use of, incorporates, or is associated with a particular type or category of wine.
-
C.
primaryGrapeUse
chosen
Indicates that a grape variety is primarily used for a particular purpose, such as winemaking, table consumption, or raisin production.
-
D.
traditionalWineChoice
Indicates that an entity selects or prefers a wine option that aligns with customary or historically established pairing or serving practices.
-
E.
vinificationUse
Indicates the process or method of winemaking applied to a given wine or batch.
- 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_69d87f22c7248190a54c949738441e2e |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2491821d0819086cffdd7551ba85a |
completed | April 17, 2026, 2:52 p.m. |
| PD | Predicate disambiguation | batch_69e219f68d308190b71c1601303f0628 |
completed | April 17, 2026, 11:31 a.m. |
Created at: April 10, 2026, 5:05 a.m.