Triple
T32136008
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kazandibi |
E820764
|
entity |
| Predicate | courseInCuisine |
P119590
|
FINISHED |
| Object | Turkish cuisine |
—
|
NE NERFINISHED |
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: Turkish cuisine | Statement: [Kazandibi, courseInCuisine, Turkish cuisine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: courseInCuisine Context triple: [Kazandibi, courseInCuisine, Turkish cuisine]
-
A.
cuisineType
Indicates the type or style of food associated with an entity, such as a restaurant or dish.
-
B.
genreOfRecipes
Indicates that one entity is a genre or category that characterizes the type or style of recipes associated with another entity.
-
C.
cuisine
Indicates the type or style of food traditionally associated with or served by an entity (such as a restaurant or region).
-
D.
snackCourse
chosen
Indicates that one entity serves as a snack course in relation to another entity, typically within a meal or menu structure.
-
E.
courseCup
Indicates that a particular cup is used for or associated with serving a specific course in a meal.
- 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_69f349039e0c819091c7a7d322e3f46d |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b9a9bc4c8190a88918fc4f91136a |
completed | May 3, 2026, 2:57 a.m. |
| PD | Predicate disambiguation | batch_69f6b6293188819080d5041ca0adb969 |
completed | May 3, 2026, 2:42 a.m. |
Created at: May 1, 2026, 12:30 a.m.