Triple

T865328
Position Surface form Disambiguated ID Type / Status
Subject Russian Railways E18688 entity
Predicate serviceArea P82 FINISHED
Object Eurasia E9404 NE FINISHED

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: Eurasia | Statement: [Russian Railways, serviceArea, Eurasia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eurasia
Context triple: [Russian Railways, serviceArea, Eurasia]
  • A. Eurasia chosen
    Eurasia is the vast combined continental landmass of Europe and Asia, forming the largest continuous land area on Earth.
  • B. Europa
    Europa is a figure in Greek mythology, a Phoenician princess famously abducted by Zeus and later the eponymous queen of Crete.
  • C. North Asia
    North Asia is the vast, sparsely populated northern part of the Asian continent, dominated by Siberia and characterized by its cold climate and extensive forests and tundra.
  • D. Europe
    Europe is a diverse continent in the Northern Hemisphere known for its rich history, cultural heritage, and significant influence on global politics, economics, and science.
  • E. Asia
    Asia is a figure in Greek mythology, often considered an Oceanid nymph associated with the region that later bore her name.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69a4938ce8688190a24bdfef82ba7d21 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4ac6acc148190bcc00a1e939ace77 completed March 1, 2026, 9:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac3b99a6c08190995ece1c1e9a54c9 completed March 7, 2026, 2:52 p.m.
Created at: March 1, 2026, 7:39 p.m.