Triple

T23110891
Position Surface form Disambiguated ID Type / Status
Subject Linhas Aéreas de Moçambique E576315 entity
Predicate focusCity P164 FINISHED
Object Beira 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: Beira | Statement: [Linhas Aéreas de Moçambique, focusCity, Beira]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Beira
Context triple: [Linhas Aéreas de Moçambique, focusCity, Beira]
  • A. Beira chosen
    Beira is a major port city in central Mozambique, serving as a key commercial and transport hub for the region.
  • B. Beira (Portugal)
    Beira is a historical region in central Portugal known for its mountainous landscapes, fortified towns, and role as a traditional territorial division of the country.
  • C. Lourenço Marques
    Lourenço Marques is the former name of Maputo, the capital city and main port of Mozambique.
  • D. Porto Amboim
    Porto Amboim is a coastal municipality and port town in western Angola known for its role in regional fishing and maritime trade.
  • E. Cantanhede
    Cantanhede is a Portuguese municipality in the Centro Region known for its wine production, agricultural activity, and proximity to the Atlantic coast.
  • 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_69e245f4af548190898d434a64a1e774 completed April 17, 2026, 2:38 p.m.
NER Named-entity recognition batch_69f18e0f4d188190a9395074c630ab0d completed April 29, 2026, 4:50 a.m.
Created at: April 17, 2026, 3:58 p.m.