Triple

T14199868
Position Surface form Disambiguated ID Type / Status
Subject Mafra, Santa Catarina, Brazil E351934 entity
Predicate hasBorder P224 FINISHED
Object Papanduva
Papanduva is a municipality in the state of Santa Catarina in southern Brazil, known for its rural landscape and small-town character.
 E1088797 NE FINISHED

How this triple was built (4 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: Papanduva | Statement: [Mafra, Santa Catarina, Brazil, hasBorder, Papanduva]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Papanduva
Context triple: [Mafra, Santa Catarina, Brazil, hasBorder, Papanduva]
  • A. Terevaka
    Terevaka is a large extinct volcanic peak that forms the highest and youngest of the three main volcanoes making up Easter Island.
  • B. Piana
    Piana is a picturesque coastal village in western Corsica, France, renowned for its dramatic red granite cliffs and proximity to the UNESCO-listed Gulf of Porto and Calanche de Piana.
  • C. Ciampea
    Ciampea is a district in West Java, Indonesia, known as part of the suburban and semi-rural area surrounding the city of Bogor.
  • D. Sapopemba
    Sapopemba is a metro station on São Paulo’s Line 15–Silver monorail, serving the Sapopemba district in the city’s eastern zone.
  • E. Esla
    The Esla is a major river in northwestern Spain that flows through the provinces of León and Zamora before joining the Duero.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Papanduva
Triple: [Mafra, Santa Catarina, Brazil, hasBorder, Papanduva]
Generated description
Papanduva is a municipality in the state of Santa Catarina in southern Brazil, known for its rural landscape and small-town character.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Papanduva
Target entity description: Papanduva is a municipality in the state of Santa Catarina in southern Brazil, known for its rural landscape and small-town character.
  • A. Terevaka
    Terevaka is a large extinct volcanic peak that forms the highest and youngest of the three main volcanoes making up Easter Island.
  • B. Piana
    Piana is a picturesque coastal village in western Corsica, France, renowned for its dramatic red granite cliffs and proximity to the UNESCO-listed Gulf of Porto and Calanche de Piana.
  • C. Ciampea
    Ciampea is a district in West Java, Indonesia, known as part of the suburban and semi-rural area surrounding the city of Bogor.
  • D. Sapopemba
    Sapopemba is a metro station on São Paulo’s Line 15–Silver monorail, serving the Sapopemba district in the city’s eastern zone.
  • E. Esla
    The Esla is a major river in northwestern Spain that flows through the provinces of León and Zamora before joining the Duero.
  • F. None of above. chosen

Provenance (5 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_69d827894ac0819097803e57f3227b23 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de61f472548190a1a7edc40526eac3 completed April 14, 2026, 3:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd324b86748190b3e0a39383969cc7 completed May 8, 2026, 12:46 a.m.
NEDg Description generation batch_69fd331562308190a0a2dfcc4a0d26a0 completed May 8, 2026, 12:49 a.m.
NED2 Entity disambiguation (via description) batch_69fd338f13dc8190b264534ed9a78cb5 completed May 8, 2026, 12:51 a.m.
Created at: April 10, 2026, 1:04 a.m.