Triple

T17281606
Position Surface form Disambiguated ID Type / Status
Subject Kazansky railway terminal E419541 entity
Predicate servesDirection P12959 FINISHED
Object Ufa 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: Ufa | Statement: [Kazansky railway terminal, servesDirection, Ufa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ufa
Context triple: [Kazansky railway terminal, servesDirection, Ufa]
  • A. Ufa chosen
    Ufa is the capital and largest city of the Republic of Bashkortostan in Russia, known as a major industrial, cultural, and economic center in the Ural region.
  • B. Ulan-Ude
    Ulan-Ude is the capital city of the Republic of Buryatia in Russia, known as a major cultural and political center of the Buryat people.
  • 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. Kazanh
    Kazanh is a locality within Turkey’s Ankara Province, situated in the Central Anatolia region.
  • E. Kazan
    Kazan is a major city in western Russia and the capital of the Republic of Tatarstan, known for its rich Tatar-Russian cultural heritage and historic Kremlin.
  • 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_69d886da626481908a14ce7830329a35 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e4332a4c008190b44f4145d0e94a21 completed April 19, 2026, 1:43 a.m.
Created at: April 10, 2026, 5:40 a.m.