Triple
T8514343
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nabulsi |
E201534
|
entity |
| Predicate | usedInCuisineOf |
P25824
|
FINISHED |
| Object | Palestinian cuisine |
—
|
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: Palestinian cuisine | Statement: [Nabulsi, usedInCuisineOf, Palestinian cuisine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedInCuisineOf Context triple: [Nabulsi, usedInCuisineOf, Palestinian cuisine]
-
A.
isUsuallyCookedIn
Indicates that something is most commonly or typically prepared or cooked within a particular container, appliance, or environment.
-
B.
culinaryUse
chosen
Indicates that one entity is used in the preparation, flavoring, or serving of food or drink for another entity.
-
C.
cuisineType
Indicates the type or style of food associated with an entity, such as a restaurant or dish.
-
D.
haveCuisine
Indicates that an entity (such as a restaurant or place) offers, serves, or is associated with a particular type or style of cuisine.
-
E.
cuisine
Indicates the type or style of food traditionally associated with or served by an entity (such as a restaurant or region).
- 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_69ca8320e5748190ac2c585a0bba8193 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe60e12848190a4a3dfa457aef275 |
completed | March 31, 2026, 3:19 p.m. |
| PD | Predicate disambiguation | batch_69cbd10cfd208190a519049fad32c508 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:15 p.m.