Triple

T10087723
Position Surface form Disambiguated ID Type / Status
Subject Poospiza E215262 entity
Predicate distribution P1356 FINISHED
Object South America E1813 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: South America | Statement: [Poospiza, distribution, South America]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: South America
Context triple: [Poospiza, distribution, South America]
  • A. South America chosen
    South America is a vast, predominantly Spanish- and Portuguese-speaking continent in the Western Hemisphere known for the Amazon rainforest, Andes Mountains, and rich cultural and ecological diversity.
  • B. América
    "América" is a reflective poem by Cuban-American poet Richard Blanco that explores themes of cultural identity, family, and the immigrant experience in the United States.
  • C. América
    América is a popular Mexican professional football club based in Mexico City, widely recognized as one of the most successful and supported teams in Liga MX.
  • D. La América
    La América was a periodical associated with José Martí that played a role in disseminating his early literary and political work, including the publication of "Ismaelillo."
  • E. Las Américas
    Las Américas is a bus rapid transit station on Line 2 of Mexico City’s Metrobús system, serving passengers in the surrounding urban area.
  • 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_69ca83a1eed081908b2e9580f2ebeea7 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cdd04875748190a81d1e9ad68dda96 completed April 2, 2026, 2:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2b675f4b08190bd8285f210191b93 completed April 5, 2026, 7:22 p.m.
Created at: March 30, 2026, 9:01 p.m.