Triple

T13717282
Position Surface form Disambiguated ID Type / Status
Subject Linha de Azambuja E328933 entity
Predicate hasStation P35 FINISHED
Object Lisboa Oriente E1000913 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: Lisboa Oriente | Statement: [Linha de Azambuja, hasStation, Lisboa Oriente]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lisboa Oriente
Context triple: [Linha de Azambuja, hasStation, Lisboa Oriente]
  • A. Lisboa-Oriente chosen
    Lisboa-Oriente is a major intermodal railway and transport hub in Lisbon, Portugal, known for its modern architecture and role as a key gateway for national and international travel.
  • B. Lisboa-Santa Apolónia
    Lisboa-Santa Apolónia is one of Lisbon’s main railway termini, serving as a key national and international rail hub in Portugal.
  • C. Odivelas
    Odivelas is a suburban city and municipality in the Lisbon metropolitan area of Portugal, known for its residential character and proximity to the capital.
  • D. Loures
    Loures is a suburban municipality in the Lisbon metropolitan area of Portugal, known for its mix of urban and rural zones and its proximity to the capital city.
  • E. Oeiras
    Oeiras is a coastal municipality in the Lisbon metropolitan area of Portugal, known for its residential suburbs, business parks, and proximity to the capital.
  • 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_69d80770b9bc81909f70c8c317d53cff completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dd4398f0448190810c840a82228706 completed April 13, 2026, 7:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7b0676e50819085fe48f86c0f93d8 completed May 3, 2026, 8:30 p.m.
Created at: April 9, 2026, 9:54 p.m.