Triple
T1824799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Georgian cuisine |
E40626
|
entity |
| Predicate | hasBeverage |
P30390
|
FINISHED |
| Object | Georgian 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: Georgian wine | Statement: [Georgian cuisine, hasBeverage, Georgian wine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBeverage Context triple: [Georgian cuisine, hasBeverage, Georgian wine]
-
A.
featuresBeverage
chosen
Indicates that one entity includes, offers, or presents a particular beverage as part of its contents, services, or characteristics.
-
B.
hasTeeType
Indicates that an entity (typically a golf hole or course) is associated with a specific type or category of tee.
-
C.
traditionalDrink
Indicates that one entity is a beverage customarily consumed within the culture, heritage, or longstanding practices associated with another entity.
-
D.
stateBeverage
Indicates that an entity is a beverage associated with or designated by a particular state or region.
-
E.
favoriteDrink
Indicates that one entity has a preferred beverage over others.
- 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_69a8864644bc8190b2358ab897194ac1 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb45402688190b9a535b14030c354 |
completed | March 7, 2026, 5:15 a.m. |
| PD | Predicate disambiguation | batch_69abafd6a9948190ac2b2743db6f8f69 |
completed | March 7, 2026, 4:55 a.m. |
Created at: March 4, 2026, 7:32 p.m.