Triple

T15567343
Position Surface form Disambiguated ID Type / Status
Subject Fundão E374149 entity
Predicate hasAdministrativeCentre P1474 FINISHED
Object Fundão (city) E1133977 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 (city) | Statement: [Fundão, hasAdministrativeCentre, Fundão (city)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fundão (city)
Context triple: [Fundão, hasAdministrativeCentre, Fundão (city)]
  • A. Fundão Municipality chosen
    Fundão Municipality is a local administrative region in central Portugal known for its agricultural production, especially cherries, and its historic villages.
  • B. Flores da Cunha
    Flores da Cunha is a Brazilian municipality in the Serra Gaúcha region of Rio Grande do Sul, known for its strong Italian heritage and wine production.
  • C. Santa Isabel do Rio Negro
    Santa Isabel do Rio Negro is a remote municipality in the Brazilian state of Amazonas, located deep in the Amazon rainforest and known for its Indigenous communities and riverine environment.
  • D. Estância Velha
    Estância Velha is a municipality in the state of Rio Grande do Sul in southern Brazil, known historically for its leather and footwear industry.
  • E. Taboão da Serra
    Taboão da Serra is a densely populated municipality in the São Paulo metropolitan area in southeastern Brazil.
  • 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_69d85ccd575081908909b71a3f3e3a61 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04dde90b081908284d9258d4462e3 completed April 16, 2026, 2:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff4c4219a081909acca9f783ecd44b completed May 9, 2026, 3:01 p.m.
Created at: April 10, 2026, 4:10 a.m.