Triple

T20057975
Position Surface form Disambiguated ID Type / Status
Subject Salamansa E499390 entity
Predicate locatedOnIsland P970 FINISHED
Object São Vicente 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: São Vicente | Statement: [Salamansa, locatedOnIsland, São Vicente]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: São Vicente
Context triple: [Salamansa, locatedOnIsland, São Vicente]
  • A. São Vicente
    São Vicente is a coastal Brazilian city in the state of São Paulo, recognized as one of the country’s oldest European-founded settlements.
  • B. São Vicente
    São Vicente is a prominent island in Cape Verde known for its cultural hub Mindelo, vibrant music scene, and important Atlantic port.
  • C. São Fidélis
    São Fidélis is a municipality in the state of Rio de Janeiro, Brazil, known for its historic architecture and location along the Paraíba do Sul River.
  • D. São Vicente Island chosen
    São Vicente Island is one of the main islands of Cape Verde, known for its cultural capital Mindelo, vibrant music scene, and arid volcanic landscapes in the central Atlantic Ocean.
  • E. Ilhéus
    Ilhéus is a historic coastal city in northeastern Brazil known for its cacao production, beaches, and role as the setting of several novels by writer Jorge Amado.
  • 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_69da6276bcf48190aabbf279192a5fb4 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e6637325908190aefc0e27e2ed5750 completed April 20, 2026, 5:33 p.m.
Created at: April 11, 2026, 3:38 p.m.