Triple

T9230380
Position Surface form Disambiguated ID Type / Status
Subject Bashneft E221800 entity
Predicate refineryLocation P16607 FINISHED
Object Novo-Ufa
Novo-Ufa is an industrial locality in Russia known for hosting one of Bashneft’s major oil refineries.
 E370617 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: Novo-Ufa | Statement: [Bashneft, refineryLocation, Novo-Ufa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Novo-Ufa
Context triple: [Bashneft, refineryLocation, Novo-Ufa]
  • A. Ufa
    Ufa is the capital and largest city of the Republic of Bashkortostan in Russia, known as a major industrial, cultural, and economic center in the Ural region.
  • B. Almetyevsk
    Almetyevsk is an industrial city in the Republic of Tatarstan, Russia, known especially as a major center of the country’s oil industry.
  • C. Orenburg
    Orenburg is a major city in southwestern Russia near the Ural River, historically significant as a frontier fortress and administrative center linking European Russia with Central Asia.
  • D. Cheboksary
    Cheboksary is a major city on the Volga River in western Russia and the capital of the Chuvash Republic.
  • E. Novokuybyshevsk
    Novokuybyshevsk is an industrial city in Samara Oblast, Russia, known for its major oil refining and petrochemical industries.
  • 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: Novo-Ufa
Triple: [Bashneft, refineryLocation, Novo-Ufa]
Generated description
Novo-Ufa is an industrial locality in Russia known for hosting one of Bashneft’s major oil refineries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Novo-Ufa
Target entity description: Novo-Ufa is an industrial locality in Russia known for hosting one of Bashneft’s major oil refineries.
  • A. Ufa chosen
    Ufa is the capital and largest city of the Republic of Bashkortostan in Russia, known as a major industrial, cultural, and economic center in the Ural region.
  • B. Almetyevsk
    Almetyevsk is an industrial city in the Republic of Tatarstan, Russia, known especially as a major center of the country’s oil industry.
  • C. Orenburg
    Orenburg is a major city in southwestern Russia near the Ural River, historically significant as a frontier fortress and administrative center linking European Russia with Central Asia.
  • D. Cheboksary
    Cheboksary is a major city on the Volga River in western Russia and the capital of the Chuvash Republic.
  • E. Novokuybyshevsk
    Novokuybyshevsk is an industrial city in Samara Oblast, Russia, known for its major oil refining and petrochemical industries.
  • F. None of above.

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_69ca83ed628c8190bc02d641e57f097f completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccee17a2dc8190b373f78be7247f0d completed April 1, 2026, 10:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69d12cc1e4608190b1e93bdcf0fd9eba completed April 4, 2026, 3:22 p.m.
NEDg Description generation batch_69d12da44d7c8190afb11ae3009a79e5 completed April 4, 2026, 3:26 p.m.
NED2 Entity disambiguation (via description) batch_69d12e1459348190aed583b2364a53dd completed April 4, 2026, 3:28 p.m.
Created at: March 30, 2026, 7:29 p.m.