Triple

T17265732
Position Surface form Disambiguated ID Type / Status
Subject Обь E419121 entity
Predicate протекаетЧерез P27579 FINISHED
Object Томская область NE NERFINISHED

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: Томская область | Statement: [Обь, протекаетЧерез, Томская область]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Томская область
Context triple: [Обь, протекаетЧерез, Томская область]
  • A. Tomsk Oblast chosen
    Tomsk Oblast is a large administrative region in southwestern Siberia, Russia, known for its vast taiga forests, significant natural resources, and diverse indigenous populations.
  • B. Tyumen Oblast
    Tyumen Oblast is a large federal subject in western Siberia, Russia, known for its vast oil and gas reserves and key role in the country’s energy industry.
  • C. Omsk Oblast
    Omsk Oblast is a federal subject of southwestern Siberia in Russia, centered on the city of Omsk and known for its industrial base and agricultural production.
  • D. Irkutsk Oblast
    Irkutsk Oblast is a large federal subject of Russia in southeastern Siberia, known for its vast taiga landscapes, significant rivers, and proximity to Lake Baikal.
  • E. Novosibirsk Oblast
    Novosibirsk Oblast is a federal subject of Russia in southwestern Siberia, centered on the major city of Novosibirsk and known as an important industrial, scientific, and transportation hub.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d886d9ab108190b70edd8d17aa1204 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e42f44ec7c81909a925fc8692b0a6c completed April 19, 2026, 1:26 a.m.
Created at: April 10, 2026, 5:40 a.m.