Triple

T8376561
Position Surface form Disambiguated ID Type / Status
Subject Buenos Aires Underground Line A E197590 entity
Predicate hasStation P35 FINISHED
Object Lima
Lima is a station on Buenos Aires’ historic Underground Line A, serving passengers in the city’s central area.
 E729746 NE FINISHED

How this triple was built (4 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: [Buenos Aires Underground Line A, hasStation, Lima]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lima
Context triple: [Buenos Aires Underground Line A, hasStation, Lima]
  • A. Lima
    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 a central Madrid Metro station located in the busy commercial and entertainment hub around Plaza del Callao in the city center.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Lima
Triple: [Buenos Aires Underground Line A, hasStation, Lima]
Generated description
Lima is a station on Buenos Aires’ historic Underground Line A, serving passengers in the city’s central area.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lima
Target entity description: Lima is a station on Buenos Aires’ historic Underground Line A, serving passengers in the city’s central area.
  • A. Lima
    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. chosen

Provenance (5 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_69ca82f64c188190af4e1608036b865d completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb80c094908190afe9cc54ce4f4d58 completed March 31, 2026, 8:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69cde7f19ba08190a08cf5aea522c021 completed April 2, 2026, 3:52 a.m.
NEDg Description generation batch_69cdebf944008190b7e758ac59257e22 completed April 2, 2026, 4:09 a.m.
NED2 Entity disambiguation (via description) batch_69cdeccedf4081909cab853ee1ff1b82 completed April 2, 2026, 4:13 a.m.
Created at: March 30, 2026, 6:01 p.m.