Triple

T27221627
Position Surface form Disambiguated ID Type / Status
Subject Verkhnyaya Pyshma E681290 entity
Predicate administrativeCenterOf P383 FINISHED
Object Verkhnepyshminsky Urban Okrug
Verkhnepyshminsky Urban Okrug is a municipal formation in Sverdlovsk Oblast, Russia, that encompasses the town of Verkhnyaya Pyshma and surrounding territories under a unified local administration.
E1865218 NE FINISHED

How this triple was built (2 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: Verkhnepyshminsky Urban Okrug | Statement: [Verkhnyaya Pyshma, administrativeCenterOf, Verkhnepyshminsky Urban Okrug]
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: Verkhnepyshminsky Urban Okrug
Triple: [Verkhnyaya Pyshma, administrativeCenterOf, Verkhnepyshminsky Urban Okrug]
Generated description
Verkhnepyshminsky Urban Okrug is a municipal formation in Sverdlovsk Oblast, Russia, that encompasses the town of Verkhnyaya Pyshma and surrounding territories under a unified local administration.

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_69eefac9f64c8190a07490fe0c8b72a3 completed April 27, 2026, 5:57 a.m.
NER Named-entity recognition batch_69f6262115408190a5e1da2ed416270d completed May 2, 2026, 4:28 p.m.
NED1 Entity disambiguation (via context triple) batch_6a25c0c271088190b5d77a3a72a7187a completed June 7, 2026, 7:04 p.m.
NEDg Description generation batch_6a25cbfacf408190b7064eca075f8f1b completed June 7, 2026, 7:52 p.m.
NED2 Entity disambiguation (via description) batch_6a25d10dc9f48190b0e8ed7743f20009 completed June 7, 2026, 8:14 p.m.
Created at: April 27, 2026, 9:43 a.m.