Triple
T12284590
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Espresso Martini |
E292795
|
entity |
| Predicate | typicalEspressoType |
P104042
|
FINISHED |
| Object | freshly brewed espresso |
—
|
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: freshly brewed espresso | Statement: [Espresso Martini, typicalEspressoType, freshly brewed espresso]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalEspressoType Context triple: [Espresso Martini, typicalEspressoType, freshly brewed espresso]
-
A.
coffeeDesignationType
Indicates the specific classification or type designation assigned to a coffee (e.g., by quality, origin, or regulatory category).
-
B.
coffeeVariety
Indicates a relationship where a specific type or variety of coffee is associated with a coffee-related entity (such as a product, beverage, or plant).
-
C.
coffeeDesignation
Indicates that one entity is designated or classified as a particular type, role, or category of coffee in relation to another entity.
-
D.
teaType
Indicates the specific variety or category of tea associated with an entity.
-
E.
hasCaffeinatedOption
Indicates that something offers or includes at least one option that contains caffeine.
- 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_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. |
| PDg | Predicate description generation | batch_69d9261b7f088190b69fe6961015fce3 |
completed | April 10, 2026, 4:32 p.m. |
Created at: April 8, 2026, 9:52 p.m.