Triple

T12728938
Position Surface form Disambiguated ID Type / Status
Subject San Miguel District E304179 entity
Predicate partOf P40 FINISHED
Object Lima metropolitan area E2605 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: Lima metropolitan area | Statement: [San Miguel District, partOf, Lima metropolitan area]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lima metropolitan area
Context triple: [San Miguel District, partOf, Lima metropolitan area]
  • A. LaSalle-Peru metropolitan area
    The LaSalle-Peru metropolitan area is a small urban region in north-central Illinois centered on the twin cities of LaSalle and Peru and their surrounding communities.
  • B. Lima chosen
    Lima is the capital and largest city of Peru, known as a major political, economic, and cultural center on South America's Pacific coast.
  • C. Lima
    Lima is a station on Buenos Aires’ historic Underground Line A, serving passengers in the city’s central area.
  • D. Lima
    Lima is a subregion of Portugal’s Vinho Verde wine area, known for producing fresh, aromatic white wines from local grape varieties.
  • E. Cono Oeste of Lima
    Cono Oeste of Lima is a western metropolitan sector of Peru’s capital that groups several coastal and urban districts, including San Miguel, for planning and administrative purposes.
  • 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_69d7bdf1426c8190a4402e1c4cdec33a completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d964172490819080cd022ff8290b6e completed April 10, 2026, 8:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6fef8d94081908ea5ac426e22ef87 completed May 3, 2026, 7:53 a.m.
Created at: April 9, 2026, 5:25 p.m.