Triple

T11340199
Position Surface form Disambiguated ID Type / Status
Subject Heitor Pereira E268575 entity
Predicate familyName P18 FINISHED
Object Pereira
Pereira is a common Portuguese-language surname widely found in Brazil, Portugal, and other Lusophone communities.
 E929530 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: Pereira | Statement: [Heitor Pereira, familyName, Pereira]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Pereira
Context triple: [Heitor Pereira, familyName, Pereira]
  • A. Pereira
    Pereira is a major Colombian city known as the capital of the Risaralda department and an important economic and cultural center in the country's coffee-growing region.
  • B. Manizales
    Manizales is a mountainous Colombian city known for its coffee production, cool climate, and location in the central Andes.
  • C. Tunja
    Tunja is a historic city in central Colombia known for its well-preserved colonial architecture and cultural heritage.
  • D. Bucaramanga
    Bucaramanga is a major city in northeastern Colombia known for its mountainous setting, pleasant climate, and role as an important commercial and industrial center.
  • E. Medellín
    Medellín is Colombia’s second-largest city, known for its mountainous setting, innovative urban development, and vibrant cultural life.
  • 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: Pereira
Triple: [Heitor Pereira, familyName, Pereira]
Generated description
Pereira is a common Portuguese-language surname widely found in Brazil, Portugal, and other Lusophone communities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Pereira
Target entity description: Pereira is a common Portuguese-language surname widely found in Brazil, Portugal, and other Lusophone communities.
  • A. Pereira
    Pereira is a major Colombian city known as the capital of the Risaralda department and an important economic and cultural center in the country's coffee-growing region.
  • B. Manizales
    Manizales is a mountainous Colombian city known for its coffee production, cool climate, and location in the central Andes.
  • C. Tunja
    Tunja is a historic city in central Colombia known for its well-preserved colonial architecture and cultural heritage.
  • D. Bucaramanga
    Bucaramanga is a major city in northeastern Colombia known for its mountainous setting, pleasant climate, and role as an important commercial and industrial center.
  • E. Medellín
    Medellín is Colombia’s second-largest city, known for its mountainous setting, innovative urban development, and vibrant cultural life.
  • 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_69d6aacb1f0881908c84a349fd1be047 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7ea01c6c08190910a6ce8fb7e186d completed April 9, 2026, 6:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69e62442905881909c5228a58d9dea3d completed April 20, 2026, 1:04 p.m.
NEDg Description generation batch_69e62cf224f881908badcdab6aea1aef completed April 20, 2026, 1:41 p.m.
NED2 Entity disambiguation (via description) batch_69e663ffedfc8190a2b51995c62d1e6b completed April 20, 2026, 5:36 p.m.
Created at: April 8, 2026, 9:33 p.m.