Triple

T14199873
Position Surface form Disambiguated ID Type / Status
Subject Mafra, Santa Catarina, Brazil E351934 entity
Predicate hasBorder P224 FINISHED
Object Piên
Piên is a small municipality in the state of Paraná, southern Brazil, known for its rural landscape and proximity to the Santa Catarina border.
 E1085656 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: Piên | Statement: [Mafra, Santa Catarina, Brazil, hasBorder, Piên]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Piên
Context triple: [Mafra, Santa Catarina, Brazil, hasBorder, Piên]
  • A. Piene
    Piene is a German surname most notably associated with Otto Piene, a pioneering artist and co-founder of the ZERO movement.
  • B. Pias
    Pias is a civil parish located in the municipality of Serpa in Portugal’s Alentejo region.
  • C. Pitane
    Pitane was an ancient coastal city of Aeolis in western Asia Minor, known as a Greek settlement near the mouth of the Caicus River.
  • D. Pankshin
    Pankshin is a town and local government area in central Nigeria known as an administrative and educational center within Plateau State.
  • E. Piro
    Piro is a town in the Bhojpur district of Bihar, India, known as one of the district’s principal urban and commercial centers.
  • 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: Piên
Triple: [Mafra, Santa Catarina, Brazil, hasBorder, Piên]
Generated description
Piên is a small municipality in the state of Paraná, southern Brazil, known for its rural landscape and proximity to the Santa Catarina border.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Piên
Target entity description: Piên is a small municipality in the state of Paraná, southern Brazil, known for its rural landscape and proximity to the Santa Catarina border.
  • A. Piene
    Piene is a German surname most notably associated with Otto Piene, a pioneering artist and co-founder of the ZERO movement.
  • B. Pias
    Pias is a civil parish located in the municipality of Serpa in Portugal’s Alentejo region.
  • C. Pitane
    Pitane was an ancient coastal city of Aeolis in western Asia Minor, known as a Greek settlement near the mouth of the Caicus River.
  • D. Pankshin
    Pankshin is a town and local government area in central Nigeria known as an administrative and educational center within Plateau State.
  • E. Piro
    Piro is an alternate name for the Yine language, an indigenous Arawakan language spoken in Peru.
  • 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_69fd194f13688190af7ef5ceb92a73ff completed May 7, 2026, 10:59 p.m.
NEDg Description generation batch_69fd1b198c6c81909b71a51a39711754 completed May 7, 2026, 11:07 p.m.
NED2 Entity disambiguation (via description) batch_69fd1bb585448190bc3393304980b808 completed May 7, 2026, 11:09 p.m.
Created at: April 10, 2026, 1:04 a.m.