Triple

T11786499
Position Surface form Disambiguated ID Type / Status
Subject State of Espírito Santo E280282 entity
Predicate hasCity P316 FINISHED
Object Mimoso do Sul
Mimoso do Sul is a municipality in southeastern Brazil known for its rural landscapes and agricultural-based local economy.
 E946441 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: Mimoso do Sul | Statement: [State of Espírito Santo, hasCity, Mimoso do Sul]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mimoso do Sul
Context triple: [State of Espírito Santo, hasCity, Mimoso do Sul]
  • A. Cajueiro
    Cajueiro is a neighborhood within the city of Recife in northeastern Brazil.
  • B. Juazeiro
    Juazeiro is a city in the state of Bahia, Brazil, located on the São Francisco River and known for its agricultural production and close integration with the neighboring city of Petrolina.
  • C. Hortência
    Hortência is a legendary Brazilian basketball player widely regarded as one of the greatest female players in the sport’s history.
  • D. Tamarineira
    Tamarineira is a neighborhood in the Brazilian city of Recife, known for its residential areas and local commerce.
  • E. Caieiras
    Caieiras is a municipality in the metropolitan region of São Paulo, Brazil, known for its industrial activity and surrounding green areas.
  • 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: Mimoso do Sul
Triple: [State of Espírito Santo, hasCity, Mimoso do Sul]
Generated description
Mimoso do Sul is a municipality in southeastern Brazil known for its rural landscapes and agricultural-based local economy.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mimoso do Sul
Target entity description: Mimoso do Sul is a municipality in southeastern Brazil known for its rural landscapes and agricultural-based local economy.
  • A. Cajueiro
    Cajueiro is a neighborhood within the city of Recife in northeastern Brazil.
  • B. Juazeiro
    Juazeiro is a city in the state of Bahia, Brazil, located on the São Francisco River and known for its agricultural production and close integration with the neighboring city of Petrolina.
  • C. Hortência
    Hortência is a legendary Brazilian basketball player widely regarded as one of the greatest female players in the sport’s history.
  • D. Tamarineira
    Tamarineira is a neighborhood in the Brazilian city of Recife, known for its residential areas and local commerce.
  • E. Caieiras
    Caieiras is a municipality in the metropolitan region of São Paulo, Brazil, known for its industrial activity and surrounding green areas.
  • 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_69d6ab258b808190b1735835c841e3a4 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8a586803481909af0032c35ca6e51 completed April 10, 2026, 7:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69f090e8828481908baa7f6067190db3 completed April 28, 2026, 10:50 a.m.
NEDg Description generation batch_69f0bd3f39608190b29027b30664bd9c completed April 28, 2026, 1:59 p.m.
NED2 Entity disambiguation (via description) batch_69f0ef5afd448190953b5d9929478132 completed April 28, 2026, 5:33 p.m.
Created at: April 8, 2026, 9:42 p.m.