Triple

T11893977
Position Surface form Disambiguated ID Type / Status
Subject Line 5–Lilac E282988 entity
Predicate hasStation P35 FINISHED
Object Chácara Klabin station E952231 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: Chácara Klabin station | Statement: [Line 5–Lilac, hasStation, Chácara Klabin station]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chácara Klabin station
Context triple: [Line 5–Lilac, hasStation, Chácara Klabin station]
  • A. Chácara Klabin station chosen
    Chácara Klabin station is an underground metro station in São Paulo, Brazil, serving as an important interchange point between multiple lines in the city’s subway network.
  • B. Paineiras station
    Paineiras station is an intermediate stop on Rio de Janeiro’s Corcovado Rack Railway, serving visitors en route to the Christ the Redeemer statue in the Tijuca Forest.
  • C. Cabo Ruivo station
    Cabo Ruivo station is a Lisbon Metro stop on the Red Line serving the Parque das Nações and eastern Lisbon area.
  • D. Campo Grande station
    Campo Grande station is a major Lisbon Metro interchange and transport hub in northern Lisbon, serving both the Green and Yellow lines and connecting to several bus routes.
  • E. Santa Isabel station
    Santa Isabel station is an underground stop on Santiago, Chile’s Metro system, serving Line 5 in the central area of the city.
  • 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_69d6ab2a90b08190a4e818821cc93e6d completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8dd1172988190a2c13d37220f2f93 completed April 10, 2026, 11:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69f43fe43c7c8190a85d464fd48e00d9 completed May 1, 2026, 5:53 a.m.
Created at: April 8, 2026, 9:44 p.m.