Triple

T19944845
Position Surface form Disambiguated ID Type / Status
Subject Baixa district of Lisbon E479394 entity
Predicate contains P35 FINISHED
Object Rua do Ouro NE NERFINISHED

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: Rua do Ouro | Statement: [Baixa district of Lisbon, contains, Rua do Ouro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rua do Ouro
Context triple: [Baixa district of Lisbon, contains, Rua do Ouro]
  • A. Rua do Ouro chosen
    Rua do Ouro is a historic commercial street in central Lisbon, Portugal, known for its traditional shops and location in the Baixa district.
  • B. Rua da Prata
    Rua da Prata is a historic commercial street in Lisbon’s Baixa district, known for its traditional shops and Pombaline architecture.
  • C. Rua dos Correeiros
    Rua dos Correeiros is a historic commercial street in downtown Lisbon, Portugal, known for its traditional shops and central location near major city landmarks.
  • D. Rua das Flores
    Rua das Flores is a historic street, likely located in a Portuguese-speaking city, known for its traditional architecture and urban charm.
  • E. Rua da Alfândega
    Rua da Alfândega is a historic street in Lisbon’s central Santa Maria Maior parish, known for its proximity to the old customs area along the Tagus riverfront.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e522a17c819095165d4d24939fd8 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e65a6654f481908089e1d66e17e024 completed April 20, 2026, 4:55 p.m.
Created at: April 10, 2026, 1:54 p.m.