Triple
T17891169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Broccoli |
E447318
|
entity |
| Predicate | isUsedInCuisine |
P25824
|
FINISHED |
| Object | Italian 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: Italian cuisine | Statement: [Broccoli, isUsedInCuisine, Italian cuisine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isUsedInCuisine Context triple: [Broccoli, isUsedInCuisine, Italian 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.
haveCuisine
Indicates that an entity (such as a restaurant or place) offers, serves, or is associated with a particular type or style of cuisine.
-
D.
cuisineType
Indicates the type or style of food associated with an entity, such as a restaurant or dish.
-
E.
isEatenIn
Indicates that one entity (typically food) is consumed within the context, location, or occasion specified by another entity.
- 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_69d8b9f59bd48190a6fc925a855b8bac |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49d7948888190b59ddc7061d13e84 |
completed | April 19, 2026, 9:16 a.m. |
| PD | Predicate disambiguation | batch_69e3d8e9b77c8190bbfb508f28dfacfa |
completed | April 18, 2026, 7:18 p.m. |
Created at: April 10, 2026, 10:19 a.m.