Triple

T20405030
Position Surface form Disambiguated ID Type / Status
Subject SL Tvärbanan E500444 entity
Predicate cityServed P82 FINISHED
Object Stockholm NE NERFINISHED

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: Stockholm | Statement: [SL Tvärbanan, cityServed, Stockholm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Stockholm
Context triple: [SL Tvärbanan, cityServed, Stockholm]
  • A. Stockholm
    Stockholm is a fictional character in the Spanish television series "Money Heist" (La Casa de Papel), known for evolving from a hostage to a member of the heist crew.
  • B. Stockholm
    Stockholm is a small village in southeastern Saskatchewan, Canada, known for its agricultural surroundings and rural community character.
  • C. Stockholm chosen
    Stockholm is the capital city of Sweden, renowned for its historic architecture, cultural institutions, and role as a major political, economic, and scientific center in Scandinavia.
  • D. Gothenburg
    Gothenburg is Sweden’s second-largest city, a major port on the country’s west coast known for its maritime heritage, universities, and vibrant cultural scene.
  • E. Gothenburg
    Gothenburg is a small city in western Nebraska known for its historic Pony Express station and classic Midwestern agricultural community.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4a81bec8190b69adfdc1336a015 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6799161c48190825eca3027d1aa51 completed April 20, 2026, 7:08 p.m.
Created at: April 16, 2026, 11:29 a.m.