Triple
T11601237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lux Row Distillers |
E275133
|
entity |
| Predicate | hasAlcoholicBeverageCategory |
P14783
|
FINISHED |
| Object | bourbon |
—
|
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: bourbon | Statement: [Lux Row Distillers, hasAlcoholicBeverageCategory, bourbon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAlcoholicBeverageCategory Context triple: [Lux Row Distillers, hasAlcoholicBeverageCategory, bourbon]
-
A.
isAlcoholicBeverage
Indicates that a beverage contains alcohol and is classified as an alcoholic drink.
-
B.
hasBeverageCategory
Indicates that an entity is associated with or classified under a particular beverage category.
-
C.
madeWithAlcohol
Indicates that something is created, prepared, or produced using alcohol as an ingredient or component.
-
D.
alcoholType
chosen
Indicates the specific kind or category of alcohol associated with an entity (e.g., beer, wine, spirits).
-
E.
servesAlcohol
Indicates that an establishment or provider offers and supplies alcoholic beverages to customers or participants.
- 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_69d6aae6b14c81908dc5a74bad7591f9 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8954daa908190a8d532e43aa4a881 |
completed | April 10, 2026, 6:14 a.m. |
| PD | Predicate disambiguation | batch_69d85dd20d188190863d1190d4c16048 |
completed | April 10, 2026, 2:17 a.m. |
Created at: April 8, 2026, 9:38 p.m.