Triple

T25910267
Position Surface form Disambiguated ID Type / Status
Subject São José dos Pinhais E652872 entity
Predicate partOf P40 FINISHED
Object Curitiba metropolitan area
The Curitiba metropolitan area is a major urban agglomeration in southern Brazil centered on the city of Curitiba and encompassing numerous surrounding municipalities in the state of Paraná.
E1702342 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: Curitiba metropolitan area | Statement: [São José dos Pinhais, partOf, Curitiba metropolitan area]
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: Curitiba metropolitan area
Triple: [São José dos Pinhais, partOf, Curitiba metropolitan area]
Generated description
The Curitiba metropolitan area is a major urban agglomeration in southern Brazil centered on the city of Curitiba and encompassing numerous surrounding municipalities in the state of Paraná.

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_69e7ab3d3f8481909bc53ed64c06af33 completed April 21, 2026, 4:52 p.m.
NER Named-entity recognition batch_69f603c450e88190b6bb85debfac30e4 completed May 2, 2026, 2:01 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10eccb75448190b00c89280298c4f7 completed May 22, 2026, 11:54 p.m.
NEDg Description generation batch_6a10eddf8e008190a604d8c0db0fdd9d completed May 22, 2026, 11:59 p.m.
NED2 Entity disambiguation (via description) batch_6a10efe2fc188190ab9d5e8276a1ef2f completed May 23, 2026, 12:08 a.m.
Created at: April 22, 2026, 8:28 a.m.