Triple
T32820698
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canela |
E839425
|
entity |
| Predicate | regionalCuisineInfluence |
P39848
|
FINISHED |
| Object | German |
—
|
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: German | Statement: [Canela, regionalCuisineInfluence, German]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionalCuisineInfluence Context triple: [Canela, regionalCuisineInfluence, German]
-
A.
cuisineInfluence
chosen
Indicates that one cuisine has had a notable impact on the development, style, or characteristics of another cuisine.
-
B.
includesRegionalCuisine
Indicates that one entity incorporates or features the regional cuisine associated with another entity.
-
C.
regionOfCulinaryImportance
Indicates that a location is recognized for its significant culinary relevance, such as notable food traditions, specialties, or gastronomic culture.
-
D.
haveDistinctCulinaryTraditions
Indicates that the related entities possess different and distinguishable culinary practices, cuisines, or food-related customs from one another.
-
E.
influencesRegion
Indicates that one entity has an effect on, shapes, or alters the conditions, characteristics, or behavior of a specified 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_69f3493df9008190a8f5d843dcd77704 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fef8c3f2388190b995ec173512945a |
completed | May 9, 2026, 9:05 a.m. |
| PD | Predicate disambiguation | batch_69fef65975608190960b78d27e806d4f |
completed | May 9, 2026, 8:54 a.m. |
Created at: May 1, 2026, 1:15 a.m.