Triple

T19107112
Position Surface form Disambiguated ID Type / Status
Subject Nevsky Express E467683 entity
Predicate competition P563 FINISHED
Object Sapsan high-speed train 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: Sapsan high-speed train | Statement: [Nevsky Express, competition, Sapsan high-speed train]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sapsan high-speed train
Context triple: [Nevsky Express, competition, Sapsan high-speed train]
  • A. Sapsan high-speed trains chosen
    Sapsan high-speed trains are Russian high-speed passenger trains operating primarily between major cities such as Moscow and Saint Petersburg, known for significantly reducing travel times on these routes.
  • B. Aeroexpress train
    The Aeroexpress train is a dedicated high-speed rail service that connects central Moscow with major airports, including Domodedovo International Airport.
  • C. Lastochka trains
    Lastochka trains are modern Russian high-speed electric multiple units used for intercity and regional passenger services.
  • D. Yüksek Hızlı Tren
    Yüksek Hızlı Tren is Turkey’s high-speed rail service, connecting major cities with fast, modern passenger trains.
  • E. Allegro high-speed train
    The Allegro high-speed train is a tilting passenger service that operated between Helsinki, Finland, and St. Petersburg, Russia, significantly reducing travel time on this international route.
  • 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_69d8dd06a26481908039e2a1bae8c597 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5e391245c8190b1393577b61c4f76 completed April 20, 2026, 8:28 a.m.
Created at: April 10, 2026, 12:04 p.m.