Triple

T10484409
Position Surface form Disambiguated ID Type / Status
Subject Pavlovsky Uyezd E247258 entity
Predicate capital P234 FINISHED
Object Pavlovo
Pavlovo is a historic town in Russia, known as an administrative center and for its traditional metalworking and handicraft industries.
 E865639 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: Pavlovo | Statement: [Pavlovsky Uyezd, capital, Pavlovo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Pavlovo
Context triple: [Pavlovsky Uyezd, capital, Pavlovo]
  • A. Lyudinovo
    Lyudinovo is an industrial town in western Russia known for its engineering and manufacturing sectors.
  • B. Karlovo
    Karlovo is a historic town in central Bulgaria, known as the birthplace of national hero Vasil Levski and as a gateway to the Balkan Mountains.
  • C. Nova Pazova
    Nova Pazova is a town in northern Serbia known as a suburban and industrial settlement within the municipality of Stara Pazova in the Vojvodina region.
  • D. Preobrajenska
    Preobrajenska is a Russian surname most notably associated with Olga Preobrajenska, a celebrated ballerina and influential ballet teacher of the late 19th and early 20th centuries.
  • E. Zagorsk
    Zagorsk is the former Soviet-era name of the Russian town now known as Sergiyev Posad, a major center of Orthodox Christianity northeast of Moscow.
  • 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: Pavlovo
Triple: [Pavlovsky Uyezd, capital, Pavlovo]
Generated description
Pavlovo is a historic town in Russia, known as an administrative center and for its traditional metalworking and handicraft industries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Pavlovo
Target entity description: Pavlovo is a historic town in Russia, known as an administrative center and for its traditional metalworking and handicraft industries.
  • A. Lyudinovo
    Lyudinovo is an industrial town in western Russia known for its engineering and manufacturing sectors.
  • B. Karlovo
    Karlovo is a historic town in central Bulgaria, known as the birthplace of national hero Vasil Levski and as a gateway to the Balkan Mountains.
  • C. Nova Pazova
    Nova Pazova is a town in northern Serbia known as a suburban and industrial settlement within the municipality of Stara Pazova in the Vojvodina region.
  • D. Preobrajenska
    Preobrajenska is a Russian surname most notably associated with Olga Preobrajenska, a celebrated ballerina and influential ballet teacher of the late 19th and early 20th centuries.
  • E. Zagorsk
    Zagorsk is the former Soviet-era name of the Russian town now known as Sergiyev Posad, a major center of Orthodox Christianity northeast of Moscow.
  • 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_69d381c309b88190af78aa681cf6a4c2 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d50968a0bc8190a18ba24eb37431d9 completed April 7, 2026, 1:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69d8a03c647c81909521fee4a66ec8ac completed April 10, 2026, 7:01 a.m.
NEDg Description generation batch_69d8a45e5a108190ba8e6ba4af858b19 completed April 10, 2026, 7:18 a.m.
NED2 Entity disambiguation (via description) batch_69d8a890c6b081908e57cc74f18d788b completed April 10, 2026, 7:36 a.m.
Created at: April 6, 2026, 12:22 p.m.