Triple

T12954297
Position Surface form Disambiguated ID Type / Status
Subject Linha de Cascais E309970 entity
Predicate hasStation P35 FINISHED
Object Belém railway station E104056 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 railway station | Statement: [Linha de Cascais, hasStation, Belém railway station]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Belém railway station
Context triple: [Linha de Cascais, hasStation, Belém railway station]
  • A. Belém railway station chosen
    Belém railway station is a suburban train station in Lisbon, Portugal, serving the historic Belém district along the Cascais Line.
  • B. Campo Grande station
    Campo Grande station is a major Lisbon Metro interchange and transport hub in northern Lisbon, serving both the Green and Yellow lines and connecting to several bus routes.
  • C. Campo Belo station
    Campo Belo station is an underground metro station in São Paulo, Brazil, serving the city’s Line 5–Lilac.
  • D. Campo Limpo station
    Campo Limpo station is a metro station on São Paulo’s Line 5–Lilac, serving the Campo Limpo district in the city’s south zone.
  • E. São Sebastião station
    São Sebastião station is a major Lisbon Metro interchange and terminal on the Red Line, serving the São Sebastião da Pedreira area near central Lisbon.
  • 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_69d7bdfb57a88190836b743e2825feca completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d97e2b0108819098a681f93e90dbda completed April 10, 2026, 10:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6b8dc135c819091b7708d90db25cb completed May 3, 2026, 2:54 a.m.
Created at: April 9, 2026, 5:44 p.m.