Triple

T6248714
Position Surface form Disambiguated ID Type / Status
Subject Don River E139989 entity
Predicate flowsThrough P225 FINISHED
Object Lipetsk Oblast E265202 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: Lipetsk Oblast | Statement: [Don River, flowsThrough, Lipetsk Oblast]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lipetsk Oblast
Context triple: [Don River, flowsThrough, Lipetsk Oblast]
  • A. Lipetsk Oblast chosen
    Lipetsk Oblast is a federal subject of western Russia known for its industrial centers, agricultural production, and administrative capital, the city of Lipetsk.
  • B. Ryazan Oblast
    Ryazan Oblast is a federal subject of central Russia known for its historic cities, agricultural landscapes, and location along the Oka River southeast of Moscow.
  • C. Saratov Oblast
    Saratov Oblast is a federal subject of Russia located in the southeastern part of European Russia, known for its industrial centers, agricultural production, and strategic position along the Volga River.
  • D. Tambov Oblast
    Tambov Oblast is a federal subject of central Russia known for its fertile agricultural lands and location along the middle reaches of the Don River.
  • E. Penza Oblast
    Penza Oblast is a federal subject of central Russia known for its agricultural economy, mixed forests, and role as a regional industrial and cultural center.
  • 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_69c008b4858c819095b0199114a9a87b completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0633a9a048190856d5247d3b28a2e completed March 22, 2026, 9:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69dbac7a42348190a86fa5a97e3d36ca completed April 12, 2026, 2:30 p.m.
Created at: March 22, 2026, 4:24 p.m.