Triple

T4132143
Position Surface form Disambiguated ID Type / Status
Subject American Volunteer Group E85064 entity
Predicate hasAlias P455 FINISHED
Object AVG E85064 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: AVG | Statement: [American Volunteer Group, hasAlias, AVG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: AVG
Context triple: [American Volunteer Group, hasAlias, AVG]
  • A. AVG chosen
    AVG refers to the American Volunteer Group, the World War II unit of volunteer U.S. pilots famously known as the Flying Tigers who flew for China against Japan before America’s official entry into the war.
  • B. AVIRA
    AVIRA is an electronic music producer and DJ known for his melodic, progressive sound and collaborations within the trance and dance music scenes.
  • C. Avast Software s.r.o.
    Avast Software s.r.o. is a Czech cybersecurity company best known for its antivirus and internet security products for consumers and businesses worldwide.
  • D. Norton
    Norton is a residential suburb within the town of Runcorn in Cheshire, England.
  • E. Norton
    Norton is a town in Zimbabwe located near the Manyame River, known for its agricultural activities and proximity to the capital, Harare.
  • 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_69aed935ccd881909dc61f81bcdb7a78 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af022f55fc81909f2a1a04d0ea59e6 completed March 9, 2026, 5:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69b576c26d4c81909b8be74855cbd03f completed March 14, 2026, 2:54 p.m.
Created at: March 9, 2026, 3:42 p.m.