Triple
T30224748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nuremberg and surrounding Franconian region |
E768446
|
entity |
| Predicate | hasSpecialtyDrink |
P180542
|
FINISHED |
| Object | Franconian 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 wine | Statement: [Nuremberg and surrounding Franconian region, hasSpecialtyDrink, Franconian wine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSpecialtyDrink Context triple: [Nuremberg and surrounding Franconian region, hasSpecialtyDrink, Franconian wine]
-
A.
hasBeverageCategory
Indicates that an entity is associated with or classified under a particular beverage category.
-
B.
hasBeverageProduct
chosen
Indicates that one entity possesses, offers, or is associated with a particular beverage product.
-
C.
hasMixedDrinkVariant
Indicates that one item is a mixed drink that serves as a variant or version of another drink.
-
D.
hasSpecialtyFood
Indicates that an entity offers, serves, or is associated with a particular type of specialty food.
-
E.
servedDrink
Indicates that one entity provided or presented a drink to another entity.
- 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_69f2248108208190be60bf1af343ce70 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fbc9d1dba881908c399b8e1dc13ce2 |
completed | May 6, 2026, 11:08 p.m. |
| PD | Predicate disambiguation | batch_69fbc8ec03ac8190a757563f96fab283 |
completed | May 6, 2026, 11:04 p.m. |
Created at: April 29, 2026, 7:35 p.m.