Triple

T6214073
Position Surface form Disambiguated ID Type / Status
Subject Astronium E138940 entity
Predicate foundIn P40 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: [Astronium, foundIn, Brazil]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Brazil
Context triple: [Astronium, foundIn, 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_69c008ada364819096c9e92c74d639b5 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0629fd3c08190a121097c188417c4 completed March 22, 2026, 9:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69c518b75da08190829e7e746f99f5ac completed March 26, 2026, 11:29 a.m.
Created at: March 22, 2026, 4:21 p.m.