Triple

T1879855
Position Surface form Disambiguated ID Type / Status
Subject Flamengo E39827 entity
Predicate nickname P55 FINISHED
Object Mengão
Mengão is the popular nickname of Clube de Regatas do Flamengo, one of Brazil’s most successful and widely supported football clubs.
 E209790 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: Mengão | Statement: [Flamengo, nickname, Mengão]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mengão
Context triple: [Flamengo, nickname, Mengão]
  • A. Corumbá
    Corumbá is a Brazilian city in the state of Mato Grosso do Sul, known as a key gateway to the Pantanal wetlands and an important regional center for river trade and ecotourism.
  • B. Icó
    Icó is a historic municipality in northeastern Brazil known for its colonial architecture and cultural heritage within the state of Ceará.
  • C. Maracanaú
    Maracanaú is an industrial and residential city in northeastern Brazil, located in the metropolitan region of Fortaleza in the state of Ceará.
  • D. Igarassu
    Igarassu is one of Brazil’s oldest colonial towns, known for its historic churches and coastal location in the northeastern state of Pernambuco.
  • E. Parnamirim
    Parnamirim is a rapidly growing city in northeastern Brazil known for its proximity to Natal and its historical role in World War II aviation.
  • 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: Mengão
Triple: [Flamengo, nickname, Mengão]
Generated description
Mengão is the popular nickname of Clube de Regatas do Flamengo, one of Brazil’s most successful and widely supported football clubs.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mengão
Target entity description: Mengão is the popular nickname of Clube de Regatas do Flamengo, one of Brazil’s most successful and widely supported football clubs.
  • A. Corumbá
    Corumbá is a Brazilian city in the state of Mato Grosso do Sul, known as a key gateway to the Pantanal wetlands and an important regional center for river trade and ecotourism.
  • B. Icó
    Icó is a historic municipality in northeastern Brazil known for its colonial architecture and cultural heritage within the state of Ceará.
  • C. Maracanaú
    Maracanaú is an industrial and residential city in northeastern Brazil, located in the metropolitan region of Fortaleza in the state of Ceará.
  • D. Igarassu
    Igarassu is one of Brazil’s oldest colonial towns, known for its historic churches and coastal location in the northeastern state of Pernambuco.
  • E. Parnamirim
    Parnamirim is a rapidly growing city in northeastern Brazil known for its proximity to Natal and its historical role in World War II aviation.
  • 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_69a88633e4fc8190b7eb40463e048ec5 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb0fa3d388190993073ffb0f60a84 completed March 7, 2026, 5 a.m.
NED1 Entity disambiguation (via context triple) batch_69addf59a5b48190bb4b47641681ebb7 completed March 8, 2026, 8:43 p.m.
NEDg Description generation batch_69addfe604708190b85d6f7261197537 completed March 8, 2026, 8:45 p.m.
NED2 Entity disambiguation (via description) batch_69ade0919d748190a4326d4abca2c9eb completed March 8, 2026, 8:48 p.m.
Created at: March 4, 2026, 7:34 p.m.