Triple

T8376530
Position Surface form Disambiguated ID Type / Status
Subject Buenos Aires Underground Line A E197590 entity
Predicate lineDesignation P5539 FINISHED
Object Line A E191064 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: Line A | Statement: [Buenos Aires Underground Line A, lineDesignation, Line A]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Line A
Context triple: [Buenos Aires Underground Line A, lineDesignation, Line A]
  • A. Line A
    Line A is a line of the Mexico City Metro system that serves the eastern part of the metropolitan area, connecting central Mexico City with several suburban municipalities.
  • B. Line A
    Line A is the main north–south rapid transit line of the Medellín Metro system in Colombia, serving as its busiest and most central corridor.
  • C. Line A
    Line A is one of the main lines of the Prague Metro, running east–west through the city and serving several central and residential districts.
  • D. Line A chosen
    Line A is the historic first subway line of the Buenos Aires Underground, known for its early 20th-century wooden cars and route through central neighborhoods.
  • E. Line A
    Line A is one of the main tram lines serving the city of Reims, France, providing urban public transportation across key districts.
  • 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_69ca82f64c188190af4e1608036b865d completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb80c094908190afe9cc54ce4f4d58 completed March 31, 2026, 8:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69cde7f19ba08190a08cf5aea522c021 completed April 2, 2026, 3:52 a.m.
Created at: March 30, 2026, 6:01 p.m.