Triple

T26073296
Position Surface form Disambiguated ID Type / Status
Subject Verkhnyaya Sinyachikha E657608 entity
Predicate partOf P40 FINISHED
Object Alapayevsky urban okrug
Alapayevsky urban okrug is a municipal formation in Sverdlovsk Oblast, Russia, that encompasses the town of Alapayevsk and surrounding settlements under a unified local administration.
 E1817338 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: Alapayevsky urban okrug | Statement: [Verkhnyaya Sinyachikha, partOf, Alapayevsky 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: Alapayevsky urban okrug
Triple: [Verkhnyaya Sinyachikha, partOf, Alapayevsky urban okrug]
Generated description
Alapayevsky urban okrug is a municipal formation in Sverdlovsk Oblast, Russia, that encompasses the town of Alapayevsk and surrounding settlements 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_69ee5bbe539081909efc7f9dd7c1b53c completed April 26, 2026, 6:38 p.m.
NER Named-entity recognition batch_69f606ccc3b8819082ca4366aaf48a70 completed May 2, 2026, 2:14 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1632d0f8f081908eea6ced1f8dc412 completed May 26, 2026, 11:54 p.m.
NEDg Description generation batch_6a1633f6ab3c819084c6626f012a75da completed May 26, 2026, 11:59 p.m.
NED2 Entity disambiguation (via description) batch_6a16350e130c8190a9a73ee1edce0928 completed May 27, 2026, 12:04 a.m.
Created at: April 26, 2026, 7:31 p.m.