Triple

T4691375
Position Surface form Disambiguated ID Type / Status
Subject Lisbon public transport network E104040 entity
Predicate connectsTo P845 FINISHED
Object Cascais E229699 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: Cascais | Statement: [Lisbon public transport network, connectsTo, Cascais]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cascais
Context triple: [Lisbon public transport network, connectsTo, Cascais]
  • A. Aveiro
    Aveiro is a coastal city in central Portugal known for its picturesque canals, colorful moliceiro boats, and distinctive Art Nouveau architecture.
  • B. Cascais, Portugal chosen
    Cascais, Portugal is a coastal resort town near Lisbon known for its beaches, marina, and historic role as a refuge for European royalty in the 20th century.
  • C. Caldas da Rainha
    Caldas da Rainha is a historic spa and market city in western Portugal, renowned for its thermal baths, ceramics tradition, and proximity to the Atlantic coast.
  • D. Santiago do Cacém
    Santiago do Cacém is a historic municipality in Portugal’s Alentejo region, known for its medieval castle, Roman ruins at Miróbriga, and rural landscapes.
  • 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_69bd43df91f481908e9add1b617b60ef completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd639c94608190808e535d0abd08a0 completed March 20, 2026, 3:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69bfcb931f908190b72d825b95ae4861 completed March 22, 2026, 10:59 a.m.
Created at: March 20, 2026, 1:16 p.m.