Triple
T12285001
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paloma |
E292805
|
entity |
| Predicate | typicalTequilaType |
P14783
|
FINISHED |
| Object | blanco tequila |
—
|
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: blanco tequila | Statement: [Paloma, typicalTequilaType, blanco tequila]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalTequilaType Context triple: [Paloma, typicalTequilaType, blanco tequila]
-
A.
alcoholType
chosen
Indicates the specific kind or category of alcohol associated with an entity (e.g., beer, wine, spirits).
-
B.
traditionalDrink
Indicates that one entity is a beverage customarily consumed within the culture, heritage, or longstanding practices associated with another entity.
-
C.
distilleryType
Indicates the specific kind or category of distillery associated with an entity (e.g., craft, industrial, micro, or regional type).
-
D.
beerStyle
Indicates that one entity is the style or type classification of a beer associated with another entity.
-
E.
minimumAgingAñejo
Indicates the minimum aging period required for something classified as "Añejo" in the given context.
- 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.