Triple

T2511822
Position Surface form Disambiguated ID Type / Status
Subject Khimki E52717 entity
Predicate borderedBy P224 FINISHED
Object Dolgoprudny
Dolgoprudny is a town in Moscow Oblast, Russia, known for hosting the Moscow Institute of Physics and Technology and forming part of the northern suburbs of Moscow.
 E273647 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: Dolgoprudny | Statement: [Khimki, borderedBy, Dolgoprudny]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dolgoprudny
Context triple: [Khimki, borderedBy, Dolgoprudny]
  • A. Lyubertsy
    Lyubertsy is a city in Russia that serves as a major suburban and industrial center just southeast of Moscow.
  • B. Serpukhov
    Serpukhov is a historic Russian town south of Moscow known for its medieval monasteries, industrial heritage, and location on the Nara River.
  • C. Elektrostal
    Elektrostal is an industrial city in Russia known for its metallurgical and engineering industries, located east of Moscow.
  • D. Dmitrov
    Dmitrov is a historic town in Moscow Oblast, Russia, located north of Moscow and known for its medieval kremlin and role as a regional cultural center.
  • E. Noginsk
    Noginsk is a town in western Russia that serves as an industrial and transport center east of Moscow.
  • 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: Dolgoprudny
Triple: [Khimki, borderedBy, Dolgoprudny]
Generated description
Dolgoprudny is a town in Moscow Oblast, Russia, known for hosting the Moscow Institute of Physics and Technology and forming part of the northern suburbs of Moscow.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Dolgoprudny
Target entity description: Dolgoprudny is a town in Moscow Oblast, Russia, known for hosting the Moscow Institute of Physics and Technology and forming part of the northern suburbs of Moscow.
  • A. Lyubertsy
    Lyubertsy is a city in Russia that serves as a major suburban and industrial center just southeast of Moscow.
  • B. Serpukhov
    Serpukhov is a historic Russian town south of Moscow known for its medieval monasteries, industrial heritage, and location on the Nara River.
  • C. Elektrostal
    Elektrostal is an industrial city in Russia known for its metallurgical and engineering industries, located east of Moscow.
  • D. Dmitrov
    Dmitrov is a historic town in Moscow Oblast, Russia, located north of Moscow and known for its medieval kremlin and role as a regional cultural center.
  • E. Noginsk
    Noginsk is a town in western Russia that serves as an industrial and transport center east of Moscow.
  • 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_69ab4958e76481908a235377dd921c9e completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abd1efb5c48190a9b47b39a388412b completed March 7, 2026, 7:21 a.m.
NED1 Entity disambiguation (via context triple) batch_69af1fac64a881909ed527a7c50ba720 completed March 9, 2026, 7:29 p.m.
NEDg Description generation batch_69af24a761288190bc561221a535ec9b completed March 9, 2026, 7:51 p.m.
NED2 Entity disambiguation (via description) batch_69af255ab75c81909f80585c952b2c9f completed March 9, 2026, 7:54 p.m.
Created at: March 6, 2026, 9:46 p.m.