Triple

T17480895
Position Surface form Disambiguated ID Type / Status
Subject Savyolovsky railway terminal E425654 entity
Predicate connectsTo P845 FINISHED
Object Kimry 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: Kimry | Statement: [Savyolovsky railway terminal, connectsTo, Kimry]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kimry
Context triple: [Savyolovsky railway terminal, connectsTo, Kimry]
  • A. Kimry chosen
    Kimry is a small Russian town on the Volga River known historically for its shoemaking industry and wooden architecture.
  • B. Tyarlevo
    Tyarlevo is a rural locality in Russia that forms part of the Pushkinsky District near Saint Petersburg.
  • C. Vytegra
    Vytegra is a small town in northwestern Russia known as a regional center near Lake Onega and the White Sea–Baltic Canal.
  • D. Kazimayn
    Kazimayn is a major Shia Muslim pilgrimage city in Iraq, renowned for its shrine housing the tombs of the seventh and ninth Shia Imams.
  • E. Stockheim
    Stockheim is a village and district of the town of Brackenheim in the Heilbronn district of Baden-Württemberg, Germany.
  • 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_69d889dccf7481909264a1844a2e9100 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e451bfd75481908c20bc2c1cbff593 completed April 19, 2026, 3:53 a.m.
Created at: April 10, 2026, 5:48 a.m.