Triple
T22830719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New Orleans, Louisiana, United States |
E565792
|
entity |
| Predicate | hasSignatureFood |
P17971
|
FINISHED |
| Object | beignets |
—
|
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: beignets | Statement: [New Orleans, Louisiana, United States, hasSignatureFood, beignets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSignatureFood Context triple: [New Orleans, Louisiana, United States, hasSignatureFood, beignets]
-
A.
hasStapleFood
Indicates that an entity’s primary or regularly consumed basic food item is another specified entity.
-
B.
hasSpecialtyFood
chosen
Indicates that an entity offers, serves, or is associated with a particular type of specialty food.
-
C.
hasFoodOption
Indicates that an entity offers, provides, or includes a particular type of food or dining option.
-
D.
hasMealType
Indicates that an entity is associated with a specific category or type of meal (such as breakfast, lunch, or dinner).
-
E.
hasCuisineRecognition
Indicates that an entity has received formal recognition, awards, or notable acknowledgment specifically for its cuisine.
- 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_69e24585ab1c81909b2b5065d15805d5 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17e2ac8d48190b6dc7edc5ad82bc7 |
completed | April 29, 2026, 3:42 a.m. |
| PD | Predicate disambiguation | batch_69eed2d117088190acbfe130d84f8627 |
completed | April 27, 2026, 3:06 a.m. |
Created at: April 17, 2026, 3:34 p.m.