Triple
T6028757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kilchoman distillery |
E134245
|
entity |
| Predicate | typicalPeatingLevel |
P65842
|
FINISHED |
| Object | approximately 50 ppm phenols |
—
|
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: approximately 50 ppm phenols | Statement: [Kilchoman distillery, typicalPeatingLevel, approximately 50 ppm phenols]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalPeatingLevel Context triple: [Kilchoman distillery, typicalPeatingLevel, approximately 50 ppm phenols]
-
A.
hasPeatingLevelRange
chosen
Indicates that something is associated with a specified range of peatiness levels, typically expressing how peated or smoky it is within defined minimum and maximum bounds.
-
B.
tanninLevel
Indicates the degree or intensity of tannins present in or associated with something, typically a beverage like wine or tea.
-
C.
distillationType
Indicates the specific method or process of distillation used to separate or refine substances.
-
D.
hasBitternessLevel
Indicates that an entity is associated with a specific degree or intensity of bitterness.
-
E.
nicotineLevel
Indicates the amount or concentration of nicotine present in or associated with an entity.
- 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_69c0087515148190a97475d412563865 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c0560fdc84819093abba13054ea1ee |
completed | March 22, 2026, 8:50 p.m. |
| PD | Predicate disambiguation | batch_69c049e9a68c81909da0cfe4779ce9b5 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:07 p.m.