Triple
T18316366
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | African cuisine |
E438762
|
entity |
| Predicate | commonDishType |
P128481
|
FINISHED |
| Object | stew |
—
|
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: stew | Statement: [African cuisine, commonDishType, stew]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commonDishType Context triple: [African cuisine, commonDishType, stew]
-
A.
commonMealType
Indicates that two entities share the same general category or type of meal (e.g., breakfast, lunch, dinner).
-
B.
servesDish
Indicates that one entity prepares and presents a specific dish as food for another entity.
-
C.
hasDishType
Indicates that an item (such as a food or menu entry) is classified as belonging to a particular type of dish (e.g., appetizer, main course, dessert).
-
D.
genreOfRecipes
chosen
Indicates that one entity is a genre or category that characterizes the type or style of recipes associated with another entity.
-
E.
cuisineType
Indicates the type or style of food associated with an entity, such as a restaurant or dish.
- 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_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. |
Created at: April 10, 2026, 10:36 a.m.