Triple

T22690840
Position Surface form Disambiguated ID Type / Status
Subject Video Assistant Referee system E561045 entity
Predicate alsoKnownAs P39 FINISHED
Object VAR 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: VAR | Statement: [Video Assistant Referee system, alsoKnownAs, VAR]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: VAR
Context triple: [Video Assistant Referee system, alsoKnownAs, VAR]
  • A. VAR chosen
    VAR (Video Assistant Referee) is a football officiating system that uses video technology to help referees review and correct clear and obvious errors in key match situations.
  • B. VAR
    VAR is the three-letter IATA airport code assigned to Varna Airport in Varna, Bulgaria.
  • C. Var
    Var is a department in southeastern France known for its Mediterranean coastline, including popular resort areas along the French Riviera.
  • D. Var
    Var is a Norse goddess associated with oaths, agreements, and the punishment of those who break them.
  • E. VER
    VER is the IATA airport code for General Heriberto Jara International Airport serving the city of Veracruz, Mexico.
  • 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_69e2454d71b48190a1f80af9f82b6fcf completed April 17, 2026, 2:35 p.m.
NER Named-entity recognition batch_69f1789adcc48190b4a717166d5dba19 completed April 29, 2026, 3:18 a.m.
Created at: April 17, 2026, 3:13 p.m.