Triple

T4888663
Position Surface form Disambiguated ID Type / Status
Subject San Diego metropolitan area E109503 entity
Predicate hasCity P316 FINISHED
Object San Marcos E139386 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: San Marcos | Statement: [San Diego metropolitan area, hasCity, San Marcos]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Marcos
Context triple: [San Diego metropolitan area, hasCity, San Marcos]
  • A. San Marcos
    San Marcos is a city in western Guatemala that serves as the capital of the San Marcos Department near the country’s highest peak, Volcán Tajumulco.
  • B. San Marcos
    San Marcos is a city that maintains an official twinning partnership with Biel/Bienne in Switzerland, reflecting cultural and municipal cooperation between the two communities.
  • C. San Marcos chosen
    San Marcos is a suburban city in northern San Diego County, California, known for its growing residential communities and educational institutions such as California State University San Marcos.
  • D. San Marcos, Texas
    San Marcos, Texas is a central Texas city along the San Marcos River known for its university campus, outlet shopping, and outdoor recreation.
  • E. San Antonio
    San Antonio is a barangay in the municipality of Los Baños in the province of Laguna, Philippines.
  • 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_69bd440f71348190b99938e59fb7f9a1 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6e053db8819087828e753c78d341 completed March 20, 2026, 3:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69be681704f08190938aec498d7d4662 completed March 21, 2026, 9:42 a.m.
Created at: March 20, 2026, 1:28 p.m.