Triple

T4679779
Position Surface form Disambiguated ID Type / Status
Subject San Diego Trolley E103770 entity
Predicate serves P98 FINISHED
Object El Cajon E201775 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: El Cajon | Statement: [San Diego Trolley, serves, El Cajon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: El Cajon
Context triple: [San Diego Trolley, serves, El Cajon]
  • A. El Cajon chosen
    El Cajon is a suburban city in Southern California’s East County region, located just east of San Diego.
  • B. Escondido
    Escondido is a city in northern San Diego County, California, known as one of the region’s older inland communities with a mix of suburban neighborhoods, agriculture, and historic downtown areas.
  • C. Fullerton
    Fullerton is a major Chicago Transit Authority station in the Lincoln Park neighborhood that serves multiple 'L' lines and connects riders to both local destinations and downtown Chicago.
  • D. Fullerton
    Fullerton is a city in northern Orange County, California, known for its historic downtown, California State University campus, and role as a suburban hub within the Greater Los Angeles metropolitan area.
  • 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_69bd43dda32c8190938b37744ca270fc completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd636c105081908655ab384f539f38 completed March 20, 2026, 3:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69be39ca8f6081909aecde545f211bb9 completed March 21, 2026, 6:25 a.m.
Created at: March 20, 2026, 1:16 p.m.