Triple

T4824124
Position Surface form Disambiguated ID Type / Status
Subject Airborne Forces of the Russian Federation E107780 entity
Predicate country P26 FINISHED
Object Russian Federation E10011 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: Russian Federation | Statement: [Airborne Forces of the Russian Federation, country, Russian Federation]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Russian Federation
Context triple: [Airborne Forces of the Russian Federation, country, Russian Federation]
  • A. Russia chosen
    Russia is the world’s largest country by land area, spanning Eastern Europe and northern Asia and exerting major political, military, and cultural influence globally.
  • B. ROSSIYA
    ROSSIYA is the radio callsign used by Rossiya Airlines, a major Russian carrier based in Saint Petersburg.
  • C. Rusa
    Rusa is a genus of deer native to South and Southeast Asia, including species such as the Javan rusa and sambar.
  • D. Rusko
    Rusko is a small municipality in southwestern Finland known for its rural character and proximity to the city of Turku.
  • E. Russas
    Russas is a municipality in the northeastern Brazilian state of Ceará, known for its agricultural activities and semi-arid climate.
  • 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_69bd43fac8188190803f0327190621e4 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6cac2bd08190b3f7cfe691fed052 completed March 20, 2026, 3:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69be5c84eb4481908a7188672624bc4d completed March 21, 2026, 8:53 a.m.
Created at: March 20, 2026, 1:24 p.m.