Triple

T2851824
Position Surface form Disambiguated ID Type / Status
Subject LA Metro A Line E63109 entity
Predicate serves P98 FINISHED
Object Pasadena E8127 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: Pasadena | Statement: [LA Metro A Line, serves, Pasadena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pasadena
Context triple: [LA Metro A Line, serves, Pasadena]
  • A. Pasadena chosen
    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.
  • B. Torrance
    Torrance is a coastal city in southwestern Los Angeles County, California, known for its suburban neighborhoods, automotive and aerospace industries, and one of the region’s largest concentrations of Japanese-American residents.
  • C. Santa Monica
    Santa Monica is a coastal city in western Los Angeles County, California, known for its iconic pier, beaches, and vibrant tourism and entertainment scene.
  • D. Camarillo
    Camarillo is a suburban city in Southern California known for its mild climate, outlet shopping, and proximity to the Pacific coast.
  • 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_69ab4c407c408190857d25e027155ce9 completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abdf5ca2648190bd32c6ec4b0dd3b6 completed March 7, 2026, 8:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69b276cc19b48190a952015c9e501dd6 completed March 12, 2026, 8:18 a.m.
Created at: March 6, 2026, 10:02 p.m.