Triple

T11942114
Position Surface form Disambiguated ID Type / Status
Subject São Paulo metropolitan area E284201 entity
Predicate hasMunicipality P847 FINISHED
Object Suzano E293507 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: Suzano | Statement: [São Paulo metropolitan area, hasMunicipality, Suzano]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Suzano
Context triple: [São Paulo metropolitan area, hasMunicipality, Suzano]
  • A. Suzano chosen
    Suzano is a municipality in the eastern part of the São Paulo metropolitan region in Brazil, known for its industrial activity and integration into Greater São Paulo’s urban area.
  • B. Ensidesa
    Ensidesa was a major Spanish state-owned steel company that played a central role in Spain’s industrial development during the mid-20th century.
  • C. Mitsubishi Paper Mills
    Mitsubishi Paper Mills is a Japanese company in the Mitsubishi group that manufactures and sells a wide range of paper and paper-related products.
  • D. Compañía Manufacturera de Papeles y Cartones
    Compañía Manufacturera de Papeles y Cartones is a major Chilean paper and packaging manufacturing company that has played a significant role in the country’s industrial and economic development.
  • E. UPM
    UPM is a leading Malaysian public research university known for its strong focus on agriculture, forestry, and related scientific disciplines.
  • 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_69d6ab2db38c8190b1f0ed6663ef8ada completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d90342bb908190a019ac91a2b82f3d completed April 10, 2026, 2:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69f440a5a9c8819086a94ad60c6881b8 completed May 1, 2026, 5:56 a.m.
Created at: April 8, 2026, 9:45 p.m.