Triple

T21565458
Position Surface form Disambiguated ID Type / Status
Subject Бесы E532150 entity
Predicate hasCharacter P2308 FINISHED
Object Лямшин
Лямшин — персонаж романа Ф. М. Достоевского «Бесы», мелкий чиновник и один из участников революционного заговора, отличающийся трусливостью, суетливостью и нравственной неустойчивостью.
 E1489947 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: Лямшин | Statement: [Бесы, hasCharacter, Лямшин]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Лямшин
Context triple: [Бесы, hasCharacter, Лямшин]
  • A. Khovrino
    Khovrino is a Moscow Metro station serving as the northern terminus of the Zamoskvoretskaya Line.
  • B. Yelshanka
    Yelshanka is a locality in Russia best known as the namesake of Yelshanka railway station.
  • C. Mishaninskaya
    Mishaninskaya is a rural locality in Russia best known as the birthplace of the polymath and scientist Mikhail Lomonosov.
  • D. Krasnyi Lyman
    Krasnyi Lyman is a town in eastern Ukraine’s Donetsk Oblast, known as a regional railway hub and for its strategic location near the frontlines of the Russo-Ukrainian conflict.
  • E. Yamskaya
    Yamskaya is a name element associated with several historic streets and districts in Moscow, traditionally linked to coachmen’s settlements along major travel routes.
  • 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: Лямшин
Triple: [Бесы, hasCharacter, Лямшин]
Generated description
Лямшин — персонаж романа Ф. М. Достоевского «Бесы», мелкий чиновник и один из участников революционного заговора, отличающийся трусливостью, суетливостью и нравственной неустойчивостью.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Лямшин
Target entity description: Лямшин — персонаж романа Ф. М. Достоевского «Бесы», мелкий чиновник и один из участников революционного заговора, отличающийся трусливостью, суетливостью и нравственной неустойчивостью.
  • A. Khovrino
    Khovrino is a Moscow Metro station serving as the northern terminus of the Zamoskvoretskaya Line.
  • B. Yelshanka
    Yelshanka is a locality in Russia best known as the namesake of Yelshanka railway station.
  • C. Mishaninskaya
    Mishaninskaya is a rural locality in Russia best known as the birthplace of the polymath and scientist Mikhail Lomonosov.
  • D. Krasnyi Lyman
    Krasnyi Lyman is a town in eastern Ukraine’s Donetsk Oblast, known as a regional railway hub and for its strategic location near the frontlines of the Russo-Ukrainian conflict.
  • E. Yamskaya
    Yamskaya is a name element associated with several historic streets and districts in Moscow, traditionally linked to coachmen’s settlements along major travel routes.
  • 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_69e0c460db088190828c64206a450273 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69eed2e777c881908de84493fa939ff3 completed April 27, 2026, 3:07 a.m.
NED1 Entity disambiguation (via context triple) batch_6a09eeef32cc81908e4d6f4aebd3de1a completed May 17, 2026, 4:38 p.m.
NEDg Description generation batch_6a09efd967d481909c9e0bc1c3f18f4a completed May 17, 2026, 4:42 p.m.
NED2 Entity disambiguation (via description) batch_6a09f06905108190b77d5bd95f261f3b completed May 17, 2026, 4:44 p.m.
Created at: April 16, 2026, 6:30 p.m.