Triple

T4346299
Position Surface form Disambiguated ID Type / Status
Subject Skol E97910 entity
Predicate soldIn P1218 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: [Skol, soldIn, Brazil]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Brazil
Context triple: [Skol, soldIn, 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. 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.
  • D. Portuguesa State
    Portuguesa State is a landlocked agricultural region in western Venezuela known for its extensive plains and significant crop production, particularly of rice and corn.
  • E. Argentina
    Argentina is a large South American nation known for its diverse landscapes from the Andes to the Pampas, its vibrant culture including tango and football, and its capital city Buenos Aires.
  • 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_69b34548402c819085ab68b27c235a87 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3518d6728819084a2f40ae0bd3ac8 completed March 12, 2026, 11:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5f5ca65948190b435ecc30190a0e4 completed March 14, 2026, 11:56 p.m.
Created at: March 12, 2026, 11:15 p.m.