Triple

T22325720
Position Surface form Disambiguated ID Type / Status
Subject Perm II railway station E551895 entity
Predicate connectsTo P845 FINISHED
Object Novosibirsk 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: Novosibirsk | Statement: [Perm II railway station, connectsTo, Novosibirsk]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Novosibirsk
Context triple: [Perm II railway station, connectsTo, Novosibirsk]
  • A. Novosibirsk chosen
    Novosibirsk is a major city in southwestern Siberia and the third-largest city in Russia, known as an important industrial, scientific, and cultural center.
  • B. Sibir Novosibirsk
    Sibir Novosibirsk is a professional ice hockey club from Novosibirsk, Russia, that competes in the Kontinental Hockey League (KHL).
  • C. Omsk
    Omsk is one of the largest cities in southwestern Siberia, Russia, serving as a major industrial, cultural, and transportation hub on the Irtysh River.
  • D. Tomsk
    Tomsk is a historic university and research city in southwestern Siberia, known as one of the region’s oldest and most important cultural and educational centers.
  • E. Krasnoyarsk
    Krasnoyarsk is a large industrial and cultural city in central Russia, situated on the Yenisei River and known as one of the key urban centers of Siberia.
  • 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_69e11e482f788190b78d1588fc26d606 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15768696481909be124e86c23d551 completed April 29, 2026, 12:57 a.m.
Created at: April 16, 2026, 8:42 p.m.