Triple

T12050233
Position Surface form Disambiguated ID Type / Status
Subject Line 4–Yellow E286895 entity
Predicate hasStation P35 FINISHED
Object Faria Lima
Faria Lima is a major metro station in São Paulo, Brazil, serving the busy Avenida Brigadeiro Faria Lima corridor and connecting commuters to the city's financial and commercial districts.
E961589 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: Faria Lima | Statement: [Line 4–Yellow, hasStation, Faria Lima]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Faria Lima
Context triple: [Line 4–Yellow, hasStation, Faria Lima]
  • A. Surco
    Surco is a populous and upscale district in Lima, Peru, known for its residential areas, shopping centers, and green spaces.
  • B. Surco
    Surco is a record label known for releasing music by the Argentine-Uruguayan electronic tango collective Bajofondo and other Latin American artists.
  • C. Miraflores
    Miraflores is an upscale coastal district of Lima, Peru, known for its shopping, dining, nightlife, and cliffside views over the Pacific Ocean.
  • D. Miraflores
    Miraflores is a rural barrio (district) of the municipality of Arecibo in northern Puerto Rico.
  • E. Ilha do Retiro
    Ilha do Retiro is a football stadium in Recife, Brazil, best known as the home ground of Sport Club do Recife.
  • 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: Faria Lima
Triple: [Line 4–Yellow, hasStation, Faria Lima]
Generated description
Faria Lima is a major metro station in São Paulo, Brazil, serving the busy Avenida Brigadeiro Faria Lima corridor and connecting commuters to the city's financial and commercial districts.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Faria Lima
Target entity description: Faria Lima is a major metro station in São Paulo, Brazil, serving the busy Avenida Brigadeiro Faria Lima corridor and connecting commuters to the city's financial and commercial districts.
  • A. Surco
    Surco is a populous and upscale district in Lima, Peru, known for its residential areas, shopping centers, and green spaces.
  • B. Surco
    Surco is a record label known for releasing music by the Argentine-Uruguayan electronic tango collective Bajofondo and other Latin American artists.
  • C. Miraflores
    Miraflores is an upscale coastal district of Lima, Peru, known for its shopping, dining, nightlife, and cliffside views over the Pacific Ocean.
  • D. Miraflores
    Miraflores is a rural barrio (district) of the municipality of Arecibo in northern Puerto Rico.
  • E. Ilha do Retiro
    Ilha do Retiro is a football stadium in Recife, Brazil, best known as the home ground of Sport Club do Recife.
  • 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_69d6ab4780948190bdb9f7620c2ac27e completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d904227958819084dbd5eb2566c735 completed April 10, 2026, 2:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69f49dd140a48190844f64c228e6367a completed May 1, 2026, 12:34 p.m.
NEDg Description generation batch_69f53d95d4fc8190b5f4e460646bec2a completed May 1, 2026, 11:56 p.m.
NED2 Entity disambiguation (via description) batch_69f564b826ec819098906cf735e45093 completed May 2, 2026, 2:43 a.m.
Created at: April 8, 2026, 9:47 p.m.