Triple
T22517689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Larue |
E556690
|
entity |
| Predicate | carbonatedBeverage |
P125765
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Larue, carbonatedBeverage, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: carbonatedBeverage Context triple: [Larue, carbonatedBeverage, true]
-
A.
canBeCarbonated
Indicates that the subject is capable of being made carbonated, typically by dissolving carbon dioxide under pressure.
-
B.
isSoftDrinkVariantOf
Indicates that one soft drink is a specific version, flavor, or formulation derived from or based on another soft drink.
-
C.
isSoftDrink
chosen
Indicates that something is classified as a soft drink, typically a non-alcoholic, carbonated or sweetened beverage.
-
D.
hasCarbonatedWater
Indicates that an entity contains or is associated with carbonated (fizzy) water as a component or ingredient.
-
E.
beverageSubcategory
Indicates a more specific classification within a broader beverage category, defining the subtype or subcategory of a drink.
- 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_69e11e5657e881909f16ca58352c50da |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15e2f5c308190a0e6755a24340917 |
completed | April 29, 2026, 1:26 a.m. |
| PD | Predicate disambiguation | batch_69ee625e3b408190a60c759fb0b28fe2 |
completed | April 26, 2026, 7:07 p.m. |
Created at: April 16, 2026, 8:50 p.m.