Triple

T5013017
Position Surface form Disambiguated ID Type / Status
Subject Jerónimo de Loayza E112670 entity
Predicate workLocation P7 FINISHED
Object Lima 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 | Statement: [Jerónimo de Loayza, workLocation, Lima]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lima
Context triple: [Jerónimo de Loayza, workLocation, Lima]
  • A. 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.
  • B. Sucre
    Sucre is the constitutional capital of Bolivia, known for its well-preserved colonial architecture and historical significance in the country’s independence.
  • C. Chiclayo
    Chiclayo is a major commercial and transportation hub in northern Peru, known for its nearby archaeological sites and vibrant regional culture.
  • D. Juliaca
    Juliaca is a major commercial and transportation hub in southern Peru, known for its bustling markets and proximity to Lake Titicaca.
  • E. Callao
    Callao is Peru’s chief seaport and a major coastal city adjacent to Lima, serving as the country’s principal gateway for maritime trade.
  • 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_69bd4434acb8819086679dbeccc2fe54 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd730f12a481908a27c15dc73987c6 completed March 20, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69be9271eccc8190bbe9bdb876b41cb8 completed March 21, 2026, 12:43 p.m.
Created at: March 20, 2026, 1:35 p.m.