Triple

T4283491
Position Surface form Disambiguated ID Type / Status
Subject Novgorod E97210 entity
Predicate partOf P40 FINISHED
Object Novgorod Oblast E81852 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: Novgorod Oblast | Statement: [Novgorod, partOf, Novgorod Oblast]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Novgorod Oblast
Context triple: [Novgorod, partOf, Novgorod Oblast]
  • A. Novgorod Oblast chosen
    Novgorod Oblast is a federal subject of Russia known for its historic cities, including Veliky Novgorod, one of the oldest and most culturally significant centers in the country.
  • B. Pskov Oblast
    Pskov Oblast is a federal subject of western Russia bordering the Baltic states and Belarus, known for its historic city of Pskov and numerous medieval fortresses.
  • C. Tver Oblast
    Tver Oblast is a federal subject of western Russia known for its forests, lakes, and historic towns, and for encompassing the headwaters of major rivers including the Volga.
  • D. Yaroslavl Oblast
    Yaroslavl Oblast is a federal subject of central Russia known for its historic cities along the Volga River and its role as part of the country’s Golden Ring tourist route.
  • E. Vologda Oblast
    Vologda Oblast is a federal subject of Russia known for its historic cities, traditional wooden architecture, and significant timber and metallurgy industries in the country’s northwest.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69b3454595848190a0e6bbb6a2bea040 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3503a84548190989a96d1a30d6ef7 completed March 12, 2026, 11:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf4205d7a08190839c10bdfc476d9f completed April 3, 2026, 4:28 a.m.
Created at: March 12, 2026, 11:07 p.m.