Triple

T11813313
Position Surface form Disambiguated ID Type / Status
Subject Chistopol E280929 entity
Predicate hasNameInRussian P20560 FINISHED
Object Чистополь
Чистополь — город в Татарстане России, известный как промышленный и культурный центр, в том числе производством часов.
 E947568 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: Чистополь | Statement: [Chistopol, hasNameInRussian, Чистополь]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Чистополь
Context triple: [Chistopol, hasNameInRussian, Чистополь]
  • A. Kamyshlov
    Kamyshlov is a small historic town in Russia’s Ural region, known for its traditional wooden architecture and role as a local administrative and cultural center.
  • B. Yuryev-Polsky
    Yuryev-Polsky is a historic small town in central Russia known for its ancient white-stone churches and traditional Russian architecture.
  • C. Yalutorovsk
    Yalutorovsk is a historic town in western Siberia, Russia, known for its 17th-century origins as a fortress settlement and its location on the Tobol River.
  • D. Kuvshinovo
    Kuvshinovo is a small town in Tver Oblast, Russia, known primarily as a local industrial and administrative center.
  • E. Yegoryevsk
    Yegoryevsk is a historic town in Russia, now part of Moscow Oblast, known for its 19th-century architecture and industrial heritage.
  • 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: Чистополь
Triple: [Chistopol, hasNameInRussian, Чистополь]
Generated description
Чистополь — город в Татарстане России, известный как промышленный и культурный центр, в том числе производством часов.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Чистополь
Target entity description: Чистополь — город в Татарстане России, известный как промышленный и культурный центр, в том числе производством часов.
  • A. Kamyshlov
    Kamyshlov is a small historic town in Russia’s Ural region, known for its traditional wooden architecture and role as a local administrative and cultural center.
  • B. Yuryev-Polsky
    Yuryev-Polsky is a historic small town in central Russia known for its ancient white-stone churches and traditional Russian architecture.
  • C. Yalutorovsk
    Yalutorovsk is a historic town in western Siberia, Russia, known for its 17th-century origins as a fortress settlement and its location on the Tobol River.
  • D. Kuvshinovo
    Kuvshinovo is a small town in Tver Oblast, Russia, known primarily as a local industrial and administrative center.
  • E. Yegoryevsk
    Yegoryevsk is a historic town in Russia, now part of Moscow Oblast, known for its 19th-century architecture and industrial heritage.
  • 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_69d6ab26aae88190b2489efcb2a24234 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8a5cba708819097467bb7aca7fc65 completed April 10, 2026, 7:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69f131a01aa48190bf5a70759ac886f6 completed April 28, 2026, 10:16 p.m.
NEDg Description generation batch_69f141b31c9081908f19ff870f5f3c33 completed April 28, 2026, 11:24 p.m.
NED2 Entity disambiguation (via description) batch_69f14fdb39d48190828668fc535d7f6a completed April 29, 2026, 12:24 a.m.
Created at: April 8, 2026, 9:42 p.m.