Triple

T5096253
Position Surface form Disambiguated ID Type / Status
Subject Rus Yusupov E114872 entity
Predicate coFounded P104 FINISHED
Object Vine E17427 NE 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: Vine | Statement: [Rus Yusupov, coFounded, Vine]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vine
Context triple: [Rus Yusupov, coFounded, Vine]
  • A. Vine chosen
    Vine was a short-form video hosting service and social media platform known for its looping six-second clips and significant cultural impact in the early 2010s.
  • B. The Vine
    The Vine is a public transit service brand used by C-TRAN for its bus and related transportation services in the Vancouver, Washington area.
  • C. Vignely
    Vignely is a small French commune located in the Seine-et-Marne department in the Île-de-France region in north-central France.
  • D. Viner
    Viner is a surname most notably associated with Jacob Viner, a prominent 20th-century economist known for his influential work in international trade theory and economic history.
  • E. Vuse
    Vuse is an electronic cigarette and vaping product brand owned by British American Tobacco, known for its range of nicotine e-liquids and devices.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69bd443fc49c819089629c00e311310c completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd75652a8081908386718f1fdb1de3 completed March 20, 2026, 4:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69beba80aee081908498cbe9d4f2eaa7 completed March 21, 2026, 3:34 p.m.
Created at: March 20, 2026, 1:40 p.m.