Triple

T9533419
Position Surface form Disambiguated ID Type / Status
Subject Park Kultury E229951 entity
Predicate line P1293 FINISHED
Object Circle Line E59707 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: Circle Line | Statement: [Park Kultury, line, Circle Line]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Circle Line
Context triple: [Park Kultury, line, Circle Line]
  • A. Circle line chosen
    The Circle line is a central London Underground route forming a loop through key districts and interchanges in the city’s transport network.
  • B. Circular line
    The Circular line is a driverless rapid transit line in the Taipei Metro system that forms a loop connecting multiple districts around the city.
  • C. Circular line
    Circular line is the nickname for Line 6 of the Madrid Metro, a heavily used underground route that forms a loop around central Madrid.
  • D. Loop Line
    Loop Line is a circular rapid transit route within the Chongqing Metro system that connects multiple key districts in Chongqing, China.
  • E. Line P
    Line P is a cable car line of the Medellín Metro system that serves hillside neighborhoods by connecting them to the city’s main mass transit network.
  • 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_69ca8479934c81908006d0e6e970ae05 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd98c9fef88190beb291b41ee26066 completed April 1, 2026, 10:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69d14c4033c08190a71535b63d86f4df completed April 4, 2026, 5:37 p.m.
Created at: March 30, 2026, 8 p.m.