Triple
T16085374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kellerbier |
E390214
|
entity |
| Predicate | drinkingContext |
P121843
|
FINISHED |
| Object | regional specialty in Franconia |
—
|
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: regional specialty in Franconia | Statement: [Kellerbier, drinkingContext, regional specialty in Franconia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: drinkingContext Context triple: [Kellerbier, drinkingContext, regional specialty in Franconia]
-
A.
drinkingHabit
Indicates an entity’s typical pattern or frequency of consuming alcoholic or other beverages.
-
B.
drinkingPermitted
Indicates that consuming alcoholic beverages is allowed in a given context, location, or situation.
-
C.
drunkBy
Indicates that a substance or beverage is consumed by a particular entity.
-
D.
drunkWith
Indicates that one entity is intoxicated as a result of consuming a particular alcoholic beverage or substance associated with another entity.
-
E.
drinkWindow
Indicates the time period during which a beverage (typically wine) is considered to be at its best for drinking.
- F. None of above. chosen
Provenance (4 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_69d87f198bc48190a8b7e53ca15b7ead |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e1ff63edb0819092cbb671967bbdcd |
completed | April 17, 2026, 9:37 a.m. |
| PD | Predicate disambiguation | batch_69e1827ad7c88190b867da511cbfb7fa |
completed | April 17, 2026, 12:44 a.m. |
| PDg | Predicate description generation | batch_69e1ff5cd7e481908a29214139a3de2e |
completed | April 17, 2026, 9:37 a.m. |
Created at: April 10, 2026, 4:59 a.m.