Triple
T22787114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vefa neighborhood |
E563998
|
entity |
| Predicate | drinkSpecialty |
P4038
|
FINISHED |
| Object | boza |
—
|
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: boza | Statement: [Vefa neighborhood, drinkSpecialty, boza]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: drinkSpecialty Context triple: [Vefa neighborhood, drinkSpecialty, boza]
-
A.
traditionalDrink
chosen
Indicates that one entity is a beverage customarily consumed within the culture, heritage, or longstanding practices associated with another entity.
-
B.
beverageSubcategory
Indicates a more specific classification within a broader beverage category, defining the subtype or subcategory of a drink.
-
C.
drinks
Indicates that one entity consumes a liquid substance, typically by ingesting it through the mouth.
-
D.
drinkFamily
Indicates a familial or close relational connection between two entities centered around drinking-related activities or contexts.
-
E.
caféSpeciality
Indicates that a café is particularly known for, or specializes in, a specific product or type of offering.
- 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_69e2455500788190b4b33030461f3bbd |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17c32de6481909ef358d16de98496 |
completed | April 29, 2026, 3:34 a.m. |
| PD | Predicate disambiguation | batch_69eed2c32e8c8190b73bb9965ed47d64 |
completed | April 27, 2026, 3:06 a.m. |
Created at: April 17, 2026, 3:29 p.m.