Triple

T8035906
Position Surface form Disambiguated ID Type / Status
Subject Jonathan Pryce E187105 entity
Predicate work P12692 FINISHED
Object Brazil E102302 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: [Jonathan Pryce, work, Brazil]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Brazil
Context triple: [Jonathan Pryce, work, Brazil]
  • A. Brazil
    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 chosen
    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. Portela
    Portela is a residential parish in the municipality of Loures, within the Lisbon metropolitan area of Portugal.
  • E. 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.
  • 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_69ca82ae2d1081909dbfee42b41db419 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3ef68c6081908727d17238b3522a completed March 31, 2026, 3:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc63cb39a48190b4d987295b5aa1b2 completed April 1, 2026, 12:16 a.m.
Created at: March 30, 2026, 5:22 p.m.