Triple
T19056047
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Four Roses Distillery |
E466397
|
entity |
| Predicate | usesMashBillCount |
P94218
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Four Roses Distillery, usesMashBillCount, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesMashBillCount Context triple: [Four Roses Distillery, usesMashBillCount, 2]
-
A.
hasMashBill
chosen
Indicates that an alcoholic beverage (typically a spirit like whiskey or bourbon) is produced using a specific mixture of grains (the mash bill).
-
B.
hasApproximateNumberOfDistilleries
Indicates that an entity is associated with an estimated or approximate count of distilleries.
-
C.
numberOfBarrels
Indicates the quantity or count of barrels associated with a given entity or context.
-
D.
typicalBottleCount
Indicates the usual or standard number of bottles associated with something under normal conditions.
-
E.
hasBitternessLevel
Indicates that an entity is associated with a specific degree or intensity of bitterness.
- 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_69d8dd040fb881909af2a964f65ad208 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5dc056b708190a2b84cbddf0fc2d9 |
completed | April 20, 2026, 7:55 a.m. |
| PD | Predicate disambiguation | batch_69e4b99633c8819097988608c278ecf8 |
completed | April 19, 2026, 11:16 a.m. |
Created at: April 10, 2026, 12:03 p.m.