Triple

T2655991
Position Surface form Disambiguated ID Type / Status
Subject Russian ruble E54609 entity
Predicate region P40 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 ruble, region, Eurasia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eurasia
Context triple: [Russian ruble, region, Eurasia]
  • A. Eurasia chosen
    Eurasia is the vast combined continental landmass of Europe and Asia, forming the largest continuous land area on Earth.
  • B. Afro-Eurasia
    Afro-Eurasia is the vast continuous landmass comprising the continents of Africa, Europe, and Asia, forming the largest connected continental area on Earth.
  • C. Europa
    Europa is a figure in Greek mythology, a Phoenician princess famously abducted by Zeus and later the eponymous queen of Crete.
  • D. Europa
    Europa is one of Jupiter’s large icy moons, notable for its smooth frozen surface and the subsurface ocean that makes it a prime candidate in the search for extraterrestrial life.
  • E. 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.
  • 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_69ab49e028948190b97e01d73548b1d9 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd94ae2e881909399b3d58159aa29 completed March 7, 2026, 7:52 a.m.
NED1 Entity disambiguation (via context triple) batch_69af98d325ec819097d7f80a28343687 completed March 10, 2026, 4:06 a.m.
Created at: March 6, 2026, 9:53 p.m.