Triple

T5378125
Position Surface form Disambiguated ID Type / Status
Subject Carolina E113010 entity
Predicate hasSubdivision P747 FINISHED
Object Trujillo Bajo E260347 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: Trujillo Bajo | Statement: [Carolina, hasSubdivision, Trujillo Bajo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Trujillo Bajo
Context triple: [Carolina, hasSubdivision, Trujillo Bajo]
  • A. Trujillo Bajo chosen
    Trujillo Bajo is a historic former name for part of what is now the municipality of Carolina in northeastern Puerto Rico.
  • B. San Sebastián de Mariquita
    San Sebastián de Mariquita is a historic colonial-era town in the Tolima Department of Colombia, known for its role in the Spanish conquest and its preserved architecture.
  • C. San Vicente de Cañete
    San Vicente de Cañete is a coastal Peruvian city in the Lima Region known for its agricultural production, Afro-Peruvian cultural heritage, and role as an important commercial center in the Cañete Valley.
  • D. San Borja
    San Borja is a town in Bolivia’s Beni Department, known as a regional center in the country’s northern lowlands.
  • E. San Borja
    San Borja is a primarily residential and commercial district in Lima, Peru, known for its middle- to upper-class neighborhoods, green areas, and cultural institutions.
  • 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_69bd4436a1988190af18dcff7fd306b4 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd86cb13ac81909dc364e7d3605844 completed March 20, 2026, 5:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf411b7a808190a15ef1936a5fcfb6 completed March 22, 2026, 1:08 a.m.
Created at: March 20, 2026, 2:03 p.m.