Triple

T1172541
Position Surface form Disambiguated ID Type / Status
Subject Paraíba E24946 entity
Predicate hasHistoricCity P3786 FINISHED
Object Cabaceiras
Cabaceiras is a historic town in the Brazilian state of Paraíba, known for its well-preserved colonial architecture and frequent use as a filming location for movies and television.
 E149958 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: Cabaceiras | Statement: [Paraíba, hasHistoricCity, Cabaceiras]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Cabaceiras
Context triple: [Paraíba, hasHistoricCity, Cabaceiras]
  • A. Espinheiro
    Espinheiro is a central neighborhood in Recife, Brazil, known for its residential areas, commerce, and urban amenities.
  • B. Tamarineira
    Tamarineira is a neighborhood in the Brazilian city of Recife, known for its residential areas and local commerce.
  • C. Afogados
    Afogados is a populous neighborhood in the Brazilian city of Recife, known for its busy commercial areas and dense urban character.
  • D. Cajueiro
    Cajueiro is a neighborhood within the city of Recife in northeastern Brazil.
  • E. Engenho do Meio
    Engenho do Meio is a neighborhood located in the city of Recife, in the state of Pernambuco, Brazil.
  • 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: Cabaceiras
Triple: [Paraíba, hasHistoricCity, Cabaceiras]
Generated description
Cabaceiras is a historic town in the Brazilian state of Paraíba, known for its well-preserved colonial architecture and frequent use as a filming location for movies and television.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Cabaceiras
Target entity description: Cabaceiras is a historic town in the Brazilian state of Paraíba, known for its well-preserved colonial architecture and frequent use as a filming location for movies and television.
  • A. Espinheiro
    Espinheiro is a central neighborhood in Recife, Brazil, known for its residential areas, commerce, and urban amenities.
  • B. Tamarineira
    Tamarineira is a neighborhood in the Brazilian city of Recife, known for its residential areas and local commerce.
  • C. Afogados
    Afogados is a populous neighborhood in the Brazilian city of Recife, known for its busy commercial areas and dense urban character.
  • D. Cajueiro
    Cajueiro is a neighborhood within the city of Recife in northeastern Brazil.
  • E. Engenho do Meio
    Engenho do Meio is a neighborhood located in the city of Recife, in the state of Pernambuco, Brazil.
  • 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_69a494082a7c819095004f423f294a64 completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bcecab688190b21a926874cd98d1 completed March 1, 2026, 10:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69acbadb9758819097fd03d59ad95367 completed March 7, 2026, 11:55 p.m.
NEDg Description generation batch_69acbb7f877081909fad30dac9254a34 completed March 7, 2026, 11:57 p.m.
NED2 Entity disambiguation (via description) batch_69acbbdbab04819087b42477acbbb5f4 completed March 7, 2026, 11:59 p.m.
Created at: March 1, 2026, 7:45 p.m.