Triple

T14906292
Position Surface form Disambiguated ID Type / Status
Subject Itanhaém E360138 entity
Predicate borderedBy P224 FINISHED
Object Mongaguá E1005177 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: Mongaguá | Statement: [Itanhaém, borderedBy, Mongaguá]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mongaguá
Context triple: [Itanhaém, borderedBy, Mongaguá]
  • A. Mongaguá chosen
    Mongaguá is a coastal municipality in the state of São Paulo, Brazil, known for its beaches and tourism along the Atlantic Ocean.
  • B. Canindé
    Canindé is a municipality in the Brazilian state of Ceará known for its major religious pilgrimages honoring Saint Francis of Assisi.
  • C. Yaguará
    Yaguará is a municipality and town in the Huila Department of southwestern Colombia, known for its proximity to the Betania Reservoir and its agricultural activities.
  • D. Río Casiguaguas
    Río Casiguaguas is a river in Havana, Cuba, better known today as the Almendares River, which flows through the city and plays a key role in its history and water supply.
  • E. Quissamã
    Quissamã is a municipality in the state of Rio de Janeiro, Brazil, known for its coastal landscapes, historical sugarcane plantations, and proximity to the Campos Basin oil region.
  • 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_69d827980cbc8190a0c569ae3940a1d9 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69ded60cd5588190b1efecc2b220da69 completed April 15, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe8bcf32a48190b1f036016f2689b7 completed May 9, 2026, 1:20 a.m.
Created at: April 10, 2026, 2:12 a.m.