Triple
T12284596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Espresso Martini |
E292795
|
entity |
| Predicate | containsIngredientCategory |
P40800
|
FINISHED |
| Object | coffee |
—
|
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: coffee | Statement: [Espresso Martini, containsIngredientCategory, coffee]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsIngredientCategory Context triple: [Espresso Martini, containsIngredientCategory, coffee]
-
A.
usesIngredient
Indicates that one entity employs or incorporates another entity as an ingredient in its composition or creation.
-
B.
hasMainIngredient
Indicates that one entity is the primary or most significant ingredient used to make another entity.
-
C.
hasCuisineItem
Indicates that a particular cuisine includes, features, or is associated with a specific food item.
-
D.
ingredientType
chosen
Indicates that one entity is classified as a specific type or category of ingredient in relation to another.
-
E.
haveCuisine
Indicates that an entity (such as a restaurant or place) offers, serves, or is associated with a particular type or style of 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_69d6ab690ad081908c0ed3870ec82d53 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d9261e1570819084bb4fdb44aa6aea |
completed | April 10, 2026, 4:32 p.m. |
| PD | Predicate disambiguation | batch_69d91c4d9a9c8190aeb7beaf9792d8f0 |
completed | April 10, 2026, 3:50 p.m. |
Created at: April 8, 2026, 9:52 p.m.