Triple

T14430200
Position Surface form Disambiguated ID Type / Status
Subject Арбатско-Покровская линия E357804 entity
Predicate имеетСтанцию P726 FINISHED
Object Курская
Курская — станция Московского метрополитена, расположенная в центральной части города и обеспечивающая пересадку между несколькими линиями.
 E1102635 NE FINISHED

How this triple was built (4 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: Курская | Statement: [Арбатско-Покровская линия, имеетСтанцию, Курская]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Курская
Context triple: [Арбатско-Покровская линия, имеетСтанцию, Курская]
  • A. Volzhsky
    Volzhsky is a major industrial city in southwestern Russia located across the Volga River from Volgograd.
  • B. Swerdlov
    Swerdlov is an alternative transliteration of the Russian surname "Sverdlov," most notably associated with Bolshevik revolutionary leader Yakov Sverdlov.
  • C. Tulskaya
    Tulskaya is a Moscow Metro station on the Serpukhovsko–Timiryazevskaya Line serving the Tulskaya Square area in southern Moscow.
  • D. Voykovskaya
    Voykovskaya is a Moscow Metro station serving the Zamoskvoretskaya Line in the northern part of the city.
  • E. Chertanovskaya
    Chertanovskaya is a Moscow Metro station serving the Chertanovo district in the city’s south.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Курская
Triple: [Арбатско-Покровская линия, имеетСтанцию, Курская]
Generated description
Курская — станция Московского метрополитена, расположенная в центральной части города и обеспечивающая пересадку между несколькими линиями.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Курская
Target entity description: Курская — станция Московского метрополитена, расположенная в центральной части города и обеспечивающая пересадку между несколькими линиями.
  • A. Volzhsky
    Volzhsky is a major industrial city in southwestern Russia located across the Volga River from Volgograd.
  • B. Swerdlov
    Swerdlov is an alternative transliteration of the Russian surname "Sverdlov," most notably associated with Bolshevik revolutionary leader Yakov Sverdlov.
  • C. Tulskaya
    Tulskaya is a Moscow Metro station on the Serpukhovsko–Timiryazevskaya Line serving the Tulskaya Square area in southern Moscow.
  • D. Voykovskaya
    Voykovskaya is a Moscow Metro station serving the Zamoskvoretskaya Line in the northern part of the city.
  • E. Chertanovskaya
    Chertanovskaya is a Moscow Metro station serving the Chertanovo district in the city’s south.
  • F. None of above. chosen

Provenance (5 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_69d8279402a88190821ffa39ae15bccf completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de914570f08190b1c7c1c57a0cb476 completed April 14, 2026, 7:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd6d84fd888190b05dcf9191bae337 completed May 8, 2026, 4:58 a.m.
NEDg Description generation batch_69fd6e354c1081909afcc848ee50e5b7 completed May 8, 2026, 5:01 a.m.
NED2 Entity disambiguation (via description) batch_69fd6ee08c848190862c3ad1ef41609b completed May 8, 2026, 5:04 a.m.
Created at: April 10, 2026, 1:18 a.m.