Triple

T11786478
Position Surface form Disambiguated ID Type / Status
Subject State of Espírito Santo E280282 entity
Predicate hasCity P316 FINISHED
Object Cariacica E946429 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: Cariacica | Statement: [State of Espírito Santo, hasCity, Cariacica]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cariacica
Context triple: [State of Espírito Santo, hasCity, Cariacica]
  • A. Cumbuco
    Cumbuco is a coastal village in northeastern Brazil known for its sand dunes, lagoons, and strong winds that make it a popular destination for kitesurfing and other beach tourism.
  • B. Saquarema
    Saquarema is a coastal city in the state of Rio de Janeiro, Brazil, known for its beaches and strong surfing culture.
  • C. Parnamirim
    Parnamirim is a rapidly growing city in northeastern Brazil known for its proximity to Natal and its historical role in World War II aviation.
  • D. Vila Velha chosen
    Vila Velha is a major coastal city in southeastern Brazil known for its beaches, historic sites, and role as a key urban and economic center in the state of Espírito Santo.
  • E. Itanhaém
    Itanhaém is a coastal municipality in southeastern Brazil known for its beaches, historic colonial center, and tourism along the São Paulo state shoreline.
  • 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_69d6ab258b808190b1735835c841e3a4 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8a586803481909af0032c35ca6e51 completed April 10, 2026, 7:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69f166c735008190a73dd74dbcddd182 completed April 29, 2026, 2:02 a.m.
Created at: April 8, 2026, 9:42 p.m.