Triple
T20878947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fizzy Lifting Drinks room |
E514092
|
entity |
| Predicate | hasBeverageEffect |
P123084
|
FINISHED |
| Object | causes levitation |
—
|
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: causes levitation | Statement: [Fizzy Lifting Drinks room, hasBeverageEffect, causes levitation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBeverageEffect Context triple: [Fizzy Lifting Drinks room, hasBeverageEffect, causes levitation]
-
A.
hasBeverageReference
Indicates that one entity makes reference to, mentions, or is associated with a beverage in some way.
-
B.
hasBeverageCategory
Indicates that an entity is associated with or classified under a particular beverage category.
-
C.
effectOnDrinkers
Indicates the impact or consequences that something has on individuals who consume alcoholic beverages.
-
D.
hasEffectIn
chosen
Indicates that one entity produces, causes, or exerts an effect within a specified context, system, or environment.
-
E.
hasCaffeineContent
Indicates that one entity (typically a beverage or substance) possesses a specified amount or presence of caffeine.
- 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_69e0b4f733f081908a401c0b7beb0b9f |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c678b394819096a17de9e04cd74f |
completed | April 21, 2026, 12:36 a.m. |
| PD | Predicate disambiguation | batch_69e5c9a8dc148190b33ff51894e2a8f9 |
completed | April 20, 2026, 6:37 a.m. |
Created at: April 16, 2026, 12:45 p.m.