Triple

T1850584
Position Surface form Disambiguated ID Type / Status
Subject Central Texas E41385 entity
Predicate hasMajorCity 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: [Central Texas, hasMajorCity, San Marcos]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Marcos
Context triple: [Central Texas, hasMajorCity, 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 Antonio
    San Antonio was one of the ships in Ferdinand Magellan’s expedition fleet that participated in the first circumnavigation attempt of the globe.
  • E. San Antonio
    San Antonio is a major Chilean port city known for its significant role in the country’s maritime trade and fishing 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_69a88648cd44819093303206d96d76ad completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb066bd6881909c8d6a6b63cb0ee5 completed March 7, 2026, 4:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69adc9c5852081909e178b7e55c11bc7 completed March 8, 2026, 7:11 p.m.
Created at: March 4, 2026, 7:33 p.m.