Triple
T19617904
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gambrinus Brewery |
E470914
|
entity |
| Predicate | bitternessProfile |
P38317
|
FINISHED |
| Object | moderate bitterness |
—
|
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: moderate bitterness | Statement: [Gambrinus Brewery, bitternessProfile, moderate bitterness]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bitternessProfile Context triple: [Gambrinus Brewery, bitternessProfile, moderate bitterness]
-
A.
hasBitternessLevel
chosen
Indicates that an entity is associated with a specific degree or intensity of bitterness.
-
B.
bitterVarietyContains
Indicates that a bitter variety (such as a bitter type of a product or substance) includes or has within it a specified component or ingredient.
-
C.
bitterantType
Indicates the specific kind or category of bitterant used or associated with an entity.
-
D.
tanninLevel
Indicates the degree or intensity of tannins present in or associated with something, typically a beverage like wine or tea.
-
E.
hasTasteIntensity
Indicates the degree or strength of taste associated with something.
- 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_69d8e510fa248190b7afb274a1d4cf73 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e640e346548190b12e38d716bdfc4f |
completed | April 20, 2026, 3:06 p.m. |
| PD | Predicate disambiguation | batch_69e514e5cb108190ae260e466c447314 |
completed | April 19, 2026, 5:46 p.m. |
Created at: April 10, 2026, 1:43 p.m.