Triple

T5375905
Position Surface form Disambiguated ID Type / Status
Subject Route 15E E108958 entity
Predicate terminus P388 FINISHED
Object Belém E234072 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: Belém | Statement: [Route 15E, terminus, Belém]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Belém
Context triple: [Route 15E, terminus, Belém]
  • A. Belém
    Belém is a historic riverside district of Lisbon, Portugal, known for its monuments of the Age of Discoveries, including the Belém Tower and Jerónimos Monastery.
  • B. Belém do Pará chosen
    Belém do Pará is a major port city in northern Brazil, known as the gateway to the Amazon region and an important cultural and economic center.
  • C. São Luís
    São Luís is the historic capital of the Brazilian state of Maranhão, known for its well-preserved colonial architecture and rich Afro-Brazilian cultural heritage.
  • D. Manaus
    Manaus is a major Brazilian city and capital of the state of Amazonas, known as a key gateway to the Amazon rainforest and an important industrial and cultural center in the region.
  • E. Botucatu
    Botucatu is a municipality in southeastern Brazil known for its higher-education institutions, especially São Paulo State University (UNESP), and its surrounding sandstone cliffs and natural landscapes.
  • 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_69bd440c77948190aad2a5f39b7b80f5 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd86aed2a8819089d9e699f53563db completed March 20, 2026, 5:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf334fb498819089a33be56fa47a01 completed March 22, 2026, 12:09 a.m.
Created at: March 20, 2026, 2:03 p.m.