Triple

T15028804
Position Surface form Disambiguated ID Type / Status
Subject Pampilhosa da Serra E378286 entity
Predicate borderedBy P224 FINISHED
Object Fundão E374149 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: Fundão | Statement: [Pampilhosa da Serra, borderedBy, Fundão]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fundão
Context triple: [Pampilhosa da Serra, borderedBy, Fundão]
  • A. Fundão chosen
    Fundão is a municipality in central Portugal known for its agricultural production, particularly cherries, and its growing role as a regional technology and innovation hub.
  • B. Fundão
    Fundão is a small coastal municipality in southeastern Brazil known for its beaches and proximity to the state capital, Vitória, in Espírito Santo.
  • C. Vargem Grande
    Vargem Grande is a largely residential and semi-rural neighborhood located in the western part of Rio de Janeiro, Brazil, known for its green areas and proximity to natural reserves.
  • D. Canindé
    Canindé is a municipality in the Brazilian state of Ceará known for its major religious pilgrimages honoring Saint Francis of Assisi.
  • E. Guararema
    Guararema is a Brazilian municipality in the state of São Paulo, known for its preserved historic center, riverside landscapes, and eco-tourism attractions.
  • 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_69d85cd46b2c819090d054c27787f677 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded7e0e8c88190ac6f5786b4d4040f completed April 15, 2026, 12:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe9dd967588190821cf47e9734db21 completed May 9, 2026, 2:37 a.m.
Created at: April 10, 2026, 2:58 a.m.