Triple
T18316338
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | African cuisine |
E438762
|
entity |
| Predicate | includesRegionalCuisine |
P131312
|
FINISHED |
| Object | North African 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: North African cuisine | Statement: [African cuisine, includesRegionalCuisine, North African cuisine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesRegionalCuisine Context triple: [African cuisine, includesRegionalCuisine, North African cuisine]
-
A.
regionOfCulinaryImportance
Indicates that a location is recognized for its significant culinary relevance, such as notable food traditions, specialties, or gastronomic culture.
-
B.
haveCuisine
Indicates that an entity (such as a restaurant or place) offers, serves, or is associated with a particular type or style of cuisine.
-
C.
cuisineFeature
Indicates a characteristic, quality, or notable aspect that describes or distinguishes a particular cuisine.
-
D.
cuisineType
Indicates the type or style of food associated with an entity, such as a restaurant or dish.
-
E.
haveDistinctCulinaryTraditions
Indicates that the related entities possess different and distinguishable culinary practices, cuisines, or food-related customs from one another.
- 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_69d8b916a2d081909e249e4902f6aad9 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e5021e61008190a300b6c51976a837 |
completed | April 19, 2026, 4:26 p.m. |
| PD | Predicate disambiguation | batch_69e44fe4ee10819086b4142444fca1f5 |
completed | April 19, 2026, 3:45 a.m. |
| PDg | Predicate description generation | batch_69e451a0ba208190a5fe92832a8f7a49 |
completed | April 19, 2026, 3:53 a.m. |
Created at: April 10, 2026, 10:36 a.m.