Triple
T14336148
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nobu restaurants |
E355470
|
entity |
| Predicate | usesIngredientInfluenceFrom |
P113842
|
FINISHED |
| Object | Japanese ingredients |
—
|
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: Japanese ingredients | Statement: [Nobu restaurants, usesIngredientInfluenceFrom, Japanese ingredients]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesIngredientInfluenceFrom Context triple: [Nobu restaurants, usesIngredientInfluenceFrom, Japanese ingredients]
-
A.
usesIngredient
Indicates that one entity employs or incorporates another entity as an ingredient in its composition or creation.
-
B.
usesRecipeOf
Indicates that one entity prepares or creates something by following the recipe or formula originally defined or used by another entity.
-
C.
ingredientType
Indicates that one entity is classified as a specific type or category of ingredient in relation to another.
-
D.
culinaryUse
Indicates that one entity is used in the preparation, flavoring, or serving of food or drink for another entity.
-
E.
typicalIngredientRatio
Indicates the usual proportional relationship between different ingredients used together in a preparation or mixture.
- 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_69d8278fa2108190bc0d0e7939c1eb03 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8c2241e48190a0c626b3d741966a |
completed | April 14, 2026, 6:49 p.m. |
| PD | Predicate disambiguation | batch_69de2a9958e881909d03ac03f135163e |
completed | April 14, 2026, 11:52 a.m. |
| PDg | Predicate description generation | batch_69de2e8a40e4819080240c874da1842c |
completed | April 14, 2026, 12:09 p.m. |
Created at: April 10, 2026, 1:14 a.m.