Triple

T14387621
Position Surface form Disambiguated ID Type / Status
Subject Bryansk Oblast E356765 entity
Predicate hasCity P316 FINISHED
Object Dyatkovo
Dyatkovo is a town in western Russia known historically for its glassmaking industry and as an administrative center within Bryansk Oblast.
E1106191 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: Dyatkovo | Statement: [Bryansk Oblast, hasCity, Dyatkovo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dyatkovo
Context triple: [Bryansk Oblast, hasCity, Dyatkovo]
  • A. Kastornoye
    Kastornoye is a locality in Russia historically notable as the namesake and focal area of the Voronezh–Kastornoye military offensive during World War II.
  • B. Kuvshinovo
    Kuvshinovo is a small town in Tver Oblast, Russia, known primarily as a local industrial and administrative center.
  • C. Dubrovitsy
    Dubrovitsy is a historic rural locality in Moscow Oblast, Russia, known for its ornate baroque Church of the Theotokos of the Sign and its scenic setting near the city of Podolsk.
  • D. 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.
  • E. Makeyevka
    Makeyevka is an industrial city in eastern Ukraine’s Donetsk Oblast, historically known for its coal mining and metallurgical 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: Dyatkovo
Triple: [Bryansk Oblast, hasCity, Dyatkovo]
Generated description
Dyatkovo is a town in western Russia known historically for its glassmaking industry and as an administrative center within Bryansk Oblast.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Dyatkovo
Target entity description: Dyatkovo is a town in western Russia known historically for its glassmaking industry and as an administrative center within Bryansk Oblast.
  • A. Kastornoye
    Kastornoye is a locality in Russia historically notable as the namesake and focal area of the Voronezh–Kastornoye military offensive during World War II.
  • B. Kuvshinovo
    Kuvshinovo is a small town in Tver Oblast, Russia, known primarily as a local industrial and administrative center.
  • C. Dubrovitsy
    Dubrovitsy is a historic rural locality in Moscow Oblast, Russia, known for its ornate baroque Church of the Theotokos of the Sign and its scenic setting near the city of Podolsk.
  • D. 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.
  • E. Makeyevka
    Makeyevka is an industrial city in eastern Ukraine’s Donetsk Oblast, historically known for its coal mining and metallurgical industries.
  • 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_69d827927c988190ad98bb0360981783 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de90283b9c8190b50d30ad58bfe085 completed April 14, 2026, 7:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd8aa530fc81908aecc4439eea4c01 completed May 8, 2026, 7:03 a.m.
NEDg Description generation batch_69fd8dd4c2648190b4862085b84d9670 completed May 8, 2026, 7:16 a.m.
NED2 Entity disambiguation (via description) batch_69fd8e7dcc188190b56551dfb266c118 completed May 8, 2026, 7:19 a.m.
Created at: April 10, 2026, 1:16 a.m.