Triple

T284391
Position Surface form Disambiguated ID Type / Status
Subject Aquila E5856 entity
Predicate distribution P1356 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: [Aquila, distribution, Eurasia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eurasia
Context triple: [Aquila, distribution, Eurasia]
  • A. Eurasia chosen
    Eurasia is the vast combined continental landmass of Europe and Asia, forming the largest continuous land area on Earth.
  • B. 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.
  • C. 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.
  • D. Asia
    Asia is the world’s largest and most populous continent, encompassing diverse cultures, languages, and landscapes across the Eastern and Northern Hemispheres.
  • E. Central Asia
    Central Asia is a vast, landlocked region of Eurasia characterized by its historical Silk Road crossroads, diverse Turkic, Persian, and Russian cultural influences, and predominantly steppe and desert landscapes.
  • 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_69a25946a7ac8190a78871c210213272 completed Feb. 28, 2026, 2:56 a.m.
NER Named-entity recognition batch_69a25e0d789881908d6a9a8d6a0d4a6c completed Feb. 28, 2026, 3:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69a3cafbcc10819083680d9a24fe2a2b completed March 1, 2026, 5:13 a.m.
Created at: Feb. 28, 2026, 3:02 a.m.