Triple

T26336314
Position Surface form Disambiguated ID Type / Status
Subject Artyomovsky E662530 entity
Predicate administrativeDivision P747 FINISHED
Object Artyomovsky Urban Okrug
Artyomovsky Urban Okrug is a municipal formation in Sverdlovsk Oblast, Russia, that encompasses the town of Artyomovsky and its surrounding territory for local self-government.
E1838157 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: Artyomovsky Urban Okrug | Statement: [Artyomovsky, administrativeDivision, Artyomovsky 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: Artyomovsky Urban Okrug
Triple: [Artyomovsky, administrativeDivision, Artyomovsky Urban Okrug]
Generated description
Artyomovsky Urban Okrug is a municipal formation in Sverdlovsk Oblast, Russia, that encompasses the town of Artyomovsky and its surrounding territory for local self-government.

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_69ee81304194819092e20e0fae3aee07 completed April 26, 2026, 9:18 p.m.
NER Named-entity recognition batch_69f60f6e0dc88190b3eaa4acc7b5bb06 completed May 2, 2026, 2:51 p.m.
NED1 Entity disambiguation (via context triple) batch_6a24d3d04e9481908cd18c5fc4b239e9 completed June 7, 2026, 2:13 a.m.
NEDg Description generation batch_6a24d7f48c948190b614235728863682 completed June 7, 2026, 2:31 a.m.
NED2 Entity disambiguation (via description) batch_6a24da02305081908055992ee6c0fc56 completed June 7, 2026, 2:40 a.m.
Created at: April 26, 2026, 10:36 p.m.