Triple
T21855577
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Viva Yellow |
E539615
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object | Viva |
—
|
NE NERFINISHED |
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: Viva | Statement: [Viva Yellow, brand, Viva]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Viva Context triple: [Viva Yellow, brand, Viva]
-
A.
Viva
chosen
Viva is a bus rapid transit service in York Region, Ontario, Canada, providing frequent, limited-stop public transportation along major corridors.
-
B.
Viva
Viva is a German music television channel that gained popularity in the 1990s and 2000s for its music videos, pop culture programming, and youth-oriented shows.
-
C.
Viva Bianca
Viva Bianca is an Australian actress best known for her role as Ilithyia in the television series "Spartacus."
-
D.
Vivir
"Vivir" is a popular Latin pop ballad by Spanish singer Ricky Martin, known for its emotive lyrics and powerful vocal performance.
-
E.
Viva Maria!
Viva Maria! is a 1965 French-Italian adventure-comedy film starring Brigitte Bardot and Jeanne Moreau as two performers who become unlikely revolutionaries in Central America.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c47829648190bbe2d1d7033768ec |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f0d635b59c81908810480f3802b847 |
completed | April 28, 2026, 3:45 p.m. |
Created at: April 16, 2026, 6:56 p.m.