Triple

T15028851
Position Surface form Disambiguated ID Type / Status
Subject Pedrógão Grande E378287 entity
Predicate borderedBy P224 FINISHED
Object Pampilhosa da Serra E378286 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: Pampilhosa da Serra | Statement: [Pedrógão Grande, borderedBy, Pampilhosa da Serra]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pampilhosa da Serra
Context triple: [Pedrógão Grande, borderedBy, Pampilhosa da Serra]
  • A. Pampilhosa da Serra chosen
    Pampilhosa da Serra is a small municipality in central Portugal known for its mountainous landscapes, schist villages, and forested river valleys.
  • B. Taboão da Serra
    Taboão da Serra is a densely populated municipality in the São Paulo metropolitan area in southeastern Brazil.
  • C. Pinheiral
    Pinheiral is a small municipality in the state of Rio de Janeiro, Brazil, known for its rural character and growing role as a regional educational and residential center.
  • D. Morrinhos
    Morrinhos is a municipality in the Brazilian state of Goiás, known for its agricultural economy and regional thermal springs.
  • E. 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.
  • 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.