Triple

T12512699
Position Surface form Disambiguated ID Type / Status
Subject Kirovsky District, Leningrad Oblast E299118 entity
Predicate hasUrbanTypeSettlement P40854 FINISHED
Object Pavlovo
Pavlovo is an urban-type settlement located within the Kirovsky District of Leningrad Oblast in northwestern Russia.
E988646 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: [Kirovsky District, Leningrad Oblast, hasUrbanTypeSettlement, Pavlovo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pavlovo
Context triple: [Kirovsky District, Leningrad Oblast, hasUrbanTypeSettlement, Pavlovo]
  • A. Pavlovo
    Pavlovo is a historic town in Russia, known as an administrative center and for its traditional metalworking and handicraft industries.
  • B. Lyudinovo
    Lyudinovo is an industrial town in western Russia known for its engineering and manufacturing sectors.
  • C. 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.
  • D. 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.
  • E. 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.
  • 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: [Kirovsky District, Leningrad Oblast, hasUrbanTypeSettlement, Pavlovo]
Generated description
Pavlovo is an urban-type settlement located within the Kirovsky District of Leningrad Oblast in northwestern Russia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Pavlovo
Target entity description: Pavlovo is an urban-type settlement located within the Kirovsky District of Leningrad Oblast in northwestern Russia.
  • A. Pavlovo
    Pavlovo is a historic town in Russia, known as an administrative center and for its traditional metalworking and handicraft industries.
  • B. Lyudinovo
    Lyudinovo is an industrial town in western Russia known for its engineering and manufacturing sectors.
  • C. 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.
  • D. 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.
  • E. 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.
  • 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_69d6ada4cd388190ae3bbf83ff87057a completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d9541e752c8190bf12d2b5a37b53df completed April 10, 2026, 7:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f655745cec8190b5582eb4a339d501 completed May 2, 2026, 7:50 p.m.
NEDg Description generation batch_69f6566dccc0819085e059c7b0288f6c completed May 2, 2026, 7:54 p.m.
NED2 Entity disambiguation (via description) batch_69f657aec8fc8190b3b08ccb95595958 completed May 2, 2026, 7:59 p.m.
Created at: April 8, 2026, 9:57 p.m.