Triple

T5975541
Position Surface form Disambiguated ID Type / Status
Subject Pina E132976 entity
Predicate adjacentTo P224 FINISHED
Object Boa Viagem E135136 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: Boa Viagem | Statement: [Pina, adjacentTo, Boa Viagem]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Boa Viagem
Context triple: [Pina, adjacentTo, Boa Viagem]
  • A. Boa Viagem chosen
    Boa Viagem is a famous beachfront neighborhood in Recife, Brazil, known for its long urban beach, high-rise skyline, and vibrant tourist scene.
  • B. Congonhas
    Congonhas is a district in the city of São Paulo, Brazil, best known for giving its name to one of the country’s busiest domestic airports.
  • C. São Sebastião
    São Sebastião is a civil parish in the municipality of Ponta Delgada on São Miguel Island in Portugal’s Azores archipelago.
  • D. Santa Cruz das Flores
    Santa Cruz das Flores is the main town and administrative center of Flores Island in Portugal’s Azores archipelago.
  • E. Ponta do Sol
    Ponta do Sol is a coastal town on the island of Santo Antão in Cape Verde, known for its dramatic oceanfront setting and role as a local administrative and fishing center.
  • 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_69c0086deab081908550159ca23eec9b completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c04a3b41248190b259409f8ebb9e09 completed March 22, 2026, 7:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0e4184a708190a9e4fe8453463a4b completed March 23, 2026, 6:56 a.m.
Created at: March 22, 2026, 4:04 p.m.