Triple

T2720761
Position Surface form Disambiguated ID Type / Status
Subject CPTM commuter rail E60073 entity
Predicate connectsMunicipality P4245 FINISHED
Object Itapevi E293514 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: Itapevi | Statement: [CPTM commuter rail, connectsMunicipality, Itapevi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Itapevi
Context triple: [CPTM commuter rail, connectsMunicipality, Itapevi]
  • A. Itapevi chosen
    Itapevi is a municipality in the metropolitan region of São Paulo, Brazil, known for its growing industrial sector and residential expansion.
  • B. Combarbalá
    Combarbalá is a small Chilean town and municipality in the Coquimbo Region, known for its semi-arid landscapes, goat farming, and distinctive combarbalite stone crafts.
  • C. Icó
    Icó is a historic municipality in northeastern Brazil known for its colonial architecture and cultural heritage within the state of Ceará.
  • D. Itatiba
    Itatiba is a municipality in southeastern Brazil known for its quality of life and proximity to the metropolitan region of Campinas in the state of São Paulo.
  • E. Pirassununga
    Pirassununga is a municipality in the state of São Paulo, Brazil, known for its agricultural activities and as a site of a major University of São Paulo campus.
  • 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_69ab4b746d248190958e052045c09255 completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abdd1fc30c81909ac06588d50abdf8 completed March 7, 2026, 8:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69b01d466d6481908ca5952db369c9f3 completed March 10, 2026, 1:31 p.m.
Created at: March 6, 2026, 9:55 p.m.