Triple
T13253740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kitzingen |
E315601
|
entity |
| Predicate | hasWineSpecialty |
P95084
|
FINISHED |
| Object | Franconian Silvaner 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: Franconian Silvaner wine | Statement: [Kitzingen, hasWineSpecialty, Franconian Silvaner wine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWineSpecialty Context triple: [Kitzingen, hasWineSpecialty, Franconian Silvaner wine]
-
A.
hasWineCategory
chosen
Indicates that one entity is classified under, or associated with, a particular category or type of wine.
-
B.
hasWinery
Indicates a relationship where a subject owns, operates, or is associated with a particular winery.
-
C.
hasWineryType
Indicates the specific category or classification of winery associated with an entity.
-
D.
hasWineBrand
Indicates a relationship where an entity is associated with or offers a specific wine brand.
-
E.
hasWinemakingFacility
Indicates that an entity possesses or is associated with a facility where winemaking activities are carried out.
- 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_69d806b1072881909e46bd212259c5f0 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99cfdc9388190af1fdd3cd4717bd8 |
completed | April 11, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69d98f60911081909fa346a054f76c9f |
completed | April 11, 2026, 12:01 a.m. |
Created at: April 9, 2026, 9:24 p.m.