Triple

T21563174
Position Surface form Disambiguated ID Type / Status
Subject La Latina E532090 entity
Predicate borders P224 FINISHED
Object Lavapiés NE NERFINISHED

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: Lavapiés | Statement: [La Latina, borders, Lavapiés]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lavapiés
Context triple: [La Latina, borders, Lavapiés]
  • A. Lavapiés chosen
    Lavapiés is a central, historically working-class and now multicultural neighborhood in Madrid known for its vibrant street life, cultural venues, and diverse food scene.
  • B. Lurigancho
    Lurigancho is a district in the eastern part of Lima Province, Peru, known for its mix of urban and rural areas and for housing one of the country’s largest prisons.
  • C. San Juan de Lurigancho District
    San Juan de Lurigancho District is a populous urban district in the northeastern part of Lima, Peru, known for being one of the largest and most densely inhabited districts in the country.
  • D. Barrio Bajo
    Barrio Bajo is the lower neighborhood district of Trévélez, a mountain village in Spain’s Alpujarras region.
  • E. Almagro neighborhood
    Almagro is a traditional, centrally located neighborhood in Buenos Aires, Argentina, known for its historic tango culture, dense urban character, and vibrant local commerce.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c460db088190828c64206a450273 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69eed2e5c3348190b67003e7027efa60 completed April 27, 2026, 3:07 a.m.
Created at: April 16, 2026, 6:29 p.m.