Triple

T14430185
Position Surface form Disambiguated ID Type / Status
Subject Арбатско-Покровская линия E357804 entity
Predicate имеетКонечнуюСтанцию P34947 FINISHED
Object Щёлковская
Щёлковская — станция Московского метрополитена, расположенная на востоке города и обслуживающая крупный транспортный узел и жилые районы.
 E1100043 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: Щёлковская | Statement: [Арбатско-Покровская линия, имеетКонечнуюСтанцию, Щёлковская]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Щёлковская
Context triple: [Арбатско-Покровская линия, имеетКонечнуюСтанцию, Щёлковская]
  • A. Zarechny
    Zarechny is a small Russian city in Sverdlovsk Oblast known for its role in the region’s industrial and energy sectors.
  • B. Владимирская
    Владимирская is a station of the Saint Petersburg Metro in Russia, serving the city’s central area.
  • C. Malyovitsa
    Malyovitsa is a prominent peak in Bulgaria renowned for its rugged alpine scenery and popularity among climbers and hikers.
  • D. Вышний Волочёк
    Вышний Волочёк — это город в Тверской области России, расположенный на важном историческом водном пути между бассейнами Волги и Балтийского моря.
  • E. Olkhovaya
    Olkhovaya is a station on the Moscow Metro system, located near Prokshino on the same line.
  • 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: Щёлковская
Triple: [Арбатско-Покровская линия, имеетКонечнуюСтанцию, Щёлковская]
Generated description
Щёлковская — станция Московского метрополитена, расположенная на востоке города и обслуживающая крупный транспортный узел и жилые районы.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Щёлковская
Target entity description: Щёлковская — станция Московского метрополитена, расположенная на востоке города и обслуживающая крупный транспортный узел и жилые районы.
  • A. Zarechny
    Zarechny is a small Russian city in Sverdlovsk Oblast known for its role in the region’s industrial and energy sectors.
  • B. Владимирская
    Владимирская is a station of the Saint Petersburg Metro in Russia, serving the city’s central area.
  • C. Malyovitsa
    Malyovitsa is a prominent peak in Bulgaria renowned for its rugged alpine scenery and popularity among climbers and hikers.
  • D. Вышний Волочёк
    Вышний Волочёк — это город в Тверской области России, расположенный на важном историческом водном пути между бассейнами Волги и Балтийского моря.
  • E. Olkhovaya
    Olkhovaya is a station on the Moscow Metro system, located near Prokshino on the same line.
  • 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_69fd5bd1c4d0819085edb9ed22128b68 completed May 8, 2026, 3:43 a.m.
NEDg Description generation batch_69fd5d42e1b48190b41ecafcf9ca9a3b completed May 8, 2026, 3:49 a.m.
NED2 Entity disambiguation (via description) batch_69fd5e1ca1e081908441508d651ecc63 completed May 8, 2026, 3:53 a.m.
Created at: April 10, 2026, 1:18 a.m.