Triple

T6729185
Position Surface form Disambiguated ID Type / Status
Subject Pelé E153591 entity
Predicate countryOfBirth P1 FINISHED
Object Brazil E19289 NE FINISHED

How this triple was built (2 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: Brazil | Statement: [Pelé, countryOfBirth, Brazil]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Brazil
Context triple: [Pelé, countryOfBirth, Brazil]
  • A. Brazil chosen
    Brazil is the largest country in South America, known for its vast Amazon rainforest, diverse culture, and major cities like São Paulo and Rio de Janeiro.
  • B. Brazil
    Brazil is a 1985 dystopian science fiction film known for its darkly satirical portrayal of a bureaucratic, totalitarian society and its distinctive, surreal visual style.
  • C. Brasyl
    Brasyl is a science fiction novel by Ian McDonald that intertwines multiple timelines in Brazil to explore themes of quantum reality, culture, and globalization.
  • D. Republic of the United States of Brazil
    The Republic of the United States of Brazil was the federal republican regime that succeeded the Brazilian monarchy in 1889 and governed Brazil through much of the 20th century.
  • E. Paraguay
    Paraguay is a landlocked country in central South America known for its bilingual Spanish and Guaraní culture and its location along the Paraguay and Paraná rivers.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69c6880bdd68819097de8b6099992682 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d15591c8819082620d194eba3e9d completed March 27, 2026, 6:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7127641d08190821e7c3fbc5c3ff9 completed March 27, 2026, 11:27 p.m.
Created at: March 27, 2026, 2:08 p.m.