Triple

T260667
Position Surface form Disambiguated ID Type / Status
Subject Super Bowl XLIX E5533 entity
Predicate city P40 FINISHED
Object Glendale E25946 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: Glendale | Statement: [Super Bowl XLIX, city, Glendale]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Glendale
Context triple: [Super Bowl XLIX, city, Glendale]
  • A. Glendale, Arizona chosen
    Glendale, Arizona is a major suburb of Phoenix known for its sports and entertainment district, including professional stadiums, shopping, and annual events.
  • B. Palmdale, California
    Palmdale, California is a high-desert city in northern Los Angeles County known for its major aerospace industry presence and proximity to Edwards Air Force Base.
  • C. Pasadena
    Pasadena is a city in Los Angeles County, California, known for its scientific and cultural institutions and as the longtime host of the annual Rose Parade and Rose Bowl Game.
  • D. Oxnard, California
    Oxnard, California is a coastal city in Ventura County known for its agriculture, beaches, and role as a gateway to the Channel Islands.
  • E. Irvine
    Irvine is a master-planned city in Orange County, California, known for its affluent residential communities, strong public schools, and concentration of technology and education industries.
  • 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_69a2580a64ac8190ad76e34bb0715b5e completed Feb. 28, 2026, 2:50 a.m.
NER Named-entity recognition batch_69a25d72dad4819092c9502e6e4edc44 completed Feb. 28, 2026, 3:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69a4239b5dfc8190930c379823e42139 completed March 1, 2026, 11:31 a.m.
Created at: Feb. 28, 2026, 2:55 a.m.