Triple

T13717287
Position Surface form Disambiguated ID Type / Status
Subject Linha de Azambuja E328933 entity
Predicate hasStation P35 FINISHED
Object Azambuja E374178 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: Azambuja | Statement: [Linha de Azambuja, hasStation, Azambuja]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Azambuja
Context triple: [Linha de Azambuja, hasStation, Azambuja]
  • A. Azambuja chosen
    Azambuja is a municipality in Portugal known for its agricultural landscape and proximity to the Lisbon metropolitan area.
  • B. Luso
    Luso is the former colonial-era name of the city now known as Luena, the capital of Moxico Province in eastern Angola.
  • C. Itumbiara
    Itumbiara is a municipality in the Brazilian state of Goiás, known for its strategic location on the Paranaíba River and its role as a regional economic and transportation hub.
  • D. Morumbi
    Morumbi is a major football stadium in São Paulo, Brazil, best known as the home ground of São Paulo FC and a frequent venue for major national and international matches.
  • E. Amarante
    Amarante is a Portuguese wine subregion within Vinho Verde, known for producing fresh, often slightly sparkling white wines as well as some reds and rosés.
  • 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_69f7b8cd0280819081c524660f3822f3 completed May 3, 2026, 9:06 p.m.
Created at: April 9, 2026, 9:54 p.m.