Triple
T34005637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Virtual Diva (Remix) |
E871952
|
entity |
| Predicate | emphasizesElectronicElements |
P131197
|
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: [Virtual Diva (Remix), emphasizesElectronicElements, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: emphasizesElectronicElements Context triple: [Virtual Diva (Remix), emphasizesElectronicElements, true]
-
A.
electronicComponents
Indicates a relationship where one entity consists of, contains, or is associated with specific electronic components.
-
B.
equipmentEmphasis
Indicates that particular importance or focus is placed on certain equipment within a given context or activity.
-
C.
hasElectronicEffect
Indicates that one entity exerts or contributes an electronic influence or effect on another entity within a specified context.
-
D.
designEmphasizes
Indicates that a design intentionally places special importance or focus on a particular feature, principle, or aspect over others.
-
E.
hasElectronicCharacter
chosen
Indicates that something possesses qualities, properties, or behavior associated with electronics or electronic systems.
- 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_69f349a08848819084b348d64c1879c3 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f70b966860819089cf92927f47c5f1 |
completed | May 3, 2026, 8:47 a.m. |
| PD | Predicate disambiguation | batch_69f70abe43e08190b2a30930d96247c1 |
completed | May 3, 2026, 8:43 a.m. |
Created at: May 1, 2026, 1:50 a.m.