Triple

T6211799
Position Surface form Disambiguated ID Type / Status
Subject Rostov Oblast E138886 entity
Predicate hasCity P316 FINISHED
Object Volgodonsk
Volgodonsk is an industrial city in southwestern Russia known for its nuclear power plant and location on the Tsimlyansk Reservoir in Rostov Oblast.
 E626624 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: Volgodonsk | Statement: [Rostov Oblast, hasCity, Volgodonsk]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Volgodonsk
Context triple: [Rostov Oblast, hasCity, Volgodonsk]
  • A. Tosno
    Tosno is a town in northwestern Russia that serves as an administrative and transportation hub southeast of Saint Petersburg.
  • B. Novocherkassk
    Novocherkassk is a historic city in Russia’s Rostov Oblast that served as a key Cossack and military administrative center.
  • C. Taganrog
    Taganrog is a port city in southwestern Russia on the northern coast of the Sea of Azov, known for its maritime trade and as the birthplace of writer Anton Chekhov.
  • D. Novorossiysk
    Novorossiysk is a major port city on Russia’s Black Sea coast that serves as an important naval and commercial hub.
  • E. Gelendzhik
    Gelendzhik is a Black Sea resort city in southern Russia known for its beaches, scenic bay, and tourism infrastructure.
  • 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: Volgodonsk
Triple: [Rostov Oblast, hasCity, Volgodonsk]
Generated description
Volgodonsk is an industrial city in southwestern Russia known for its nuclear power plant and location on the Tsimlyansk Reservoir in Rostov Oblast.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Volgodonsk
Target entity description: Volgodonsk is an industrial city in southwestern Russia known for its nuclear power plant and location on the Tsimlyansk Reservoir in Rostov Oblast.
  • A. Tosno
    Tosno is a town in northwestern Russia that serves as an administrative and transportation hub southeast of Saint Petersburg.
  • B. Novocherkassk
    Novocherkassk is a historic city in Russia’s Rostov Oblast that served as a key Cossack and military administrative center.
  • C. Taganrog
    Taganrog is a port city in southwestern Russia on the northern coast of the Sea of Azov, known for its maritime trade and as the birthplace of writer Anton Chekhov.
  • D. Novorossiysk
    Novorossiysk is a major port city on Russia’s Black Sea coast that serves as an important naval and commercial hub.
  • E. Gelendzhik
    Gelendzhik is a Black Sea resort city in southern Russia known for its beaches, scenic bay, and tourism infrastructure.
  • 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_69c008ada364819096c9e92c74d639b5 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0628adccc8190b94f5c2c1d5d03f7 completed March 22, 2026, 9:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7487f26048190aeed34af6f0a8387 completed March 28, 2026, 3:18 a.m.
NEDg Description generation batch_69c749901de081908e5c3ccd324e8191 completed March 28, 2026, 3:22 a.m.
NED2 Entity disambiguation (via description) batch_69c74a0bcddc819084b22925cf57a205 completed March 28, 2026, 3:24 a.m.
Created at: March 22, 2026, 4:21 p.m.