Triple

T1157976
Position Surface form Disambiguated ID Type / Status
Subject UEFA Euro 2020 (matches) E24425 entity
Predicate usesTechnology P1485 FINISHED
Object VAR
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.
 E131113 NE FINISHED

How this triple was built (4 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: [UEFA Euro 2020 (matches), usesTechnology, VAR]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: VAR
Context triple: [UEFA Euro 2020 (matches), usesTechnology, VAR]
  • A. Var
    Var is a department in southeastern France known for its Mediterranean coastline, including popular resort areas along the French Riviera.
  • B. VIR
    VIR is the ICAO airline designator used to identify Virgin Atlantic in international aviation operations.
  • C. Varig
    Varig was Brazil’s former flagship airline, once the country’s largest carrier and a major international operator throughout much of the 20th century.
  • D. VRA
    VRA is the common abbreviation for the landmark U.S. federal law enacted in 1965 to prohibit racial discrimination in voting.
  • E. VELO
    VELO is the high-precision vertex detector of the LHCb experiment at CERN, designed to measure particle trajectories very close to the proton–proton collision point.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: VAR
Triple: [UEFA Euro 2020 (matches), usesTechnology, VAR]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: VAR
Target entity description: 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.
  • A. Var
    Var is a department in southeastern France known for its Mediterranean coastline, including popular resort areas along the French Riviera.
  • B. VIR
    VIR is the ICAO airline designator used to identify Virgin Atlantic in international aviation operations.
  • C. Varig
    Varig was Brazil’s former flagship airline, once the country’s largest carrier and a major international operator throughout much of the 20th century.
  • D. VRA
    VRA is the common abbreviation for the landmark U.S. federal law enacted in 1965 to prohibit racial discrimination in voting.
  • E. VELO
    VELO is the high-precision vertex detector of the LHCb experiment at CERN, designed to measure particle trajectories very close to the proton–proton collision point.
  • F. None of above. chosen

Provenance (5 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_69a494060e148190abb42f971242c197 completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bcab3cd08190ad06ea007042a8fc completed March 1, 2026, 10:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac5eba03f081908c8bf99e6369080a completed March 7, 2026, 5:22 p.m.
NEDg Description generation batch_69ac5f47fd0c81909c23372559b8a5c4 completed March 7, 2026, 5:24 p.m.
NED2 Entity disambiguation (via description) batch_69ac5f9b8bf48190a42307ab7ef7d803 completed March 7, 2026, 5:25 p.m.
Created at: March 1, 2026, 7:45 p.m.