Triple

T6346607
Position Surface form Disambiguated ID Type / Status
Subject Tanya E142758 entity
Predicate placeAssociated P19735 FINISHED
Object White Russia E13665 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: White Russia | Statement: [Tanya, placeAssociated, White Russia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: White Russia
Context triple: [Tanya, placeAssociated, White Russia]
  • A. Belarus chosen
    Belarus is an Eastern European country known for its flat landscapes, dense forests, and historical ties to both the Soviet Union and the broader Slavic cultural sphere.
  • B. Belorusskaya
    Belorusskaya is a Moscow Metro station that serves as a key transport hub and interchange point near Belorussky railway terminal.
  • C. La Russa
    La Russa is an Italian surname most prominently associated with Hall of Fame Major League Baseball manager Tony La Russa.
  • D. Byelorussian Soviet Socialist Republic
    The Byelorussian Soviet Socialist Republic was a constituent republic of the Soviet Union that existed from 1919 to 1991 in the territory of present-day Belarus.
  • E. Rusk
    Rusk is a surname most notably associated with Dean Rusk, who served as United States Secretary of State during the Kennedy and Johnson administrations.
  • 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_69c008d5ab108190b346c465696824a9 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c067b907c4819085a3ea87589bc4be completed March 22, 2026, 10:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69c62d4c78188190a7ceadeedd0e4d15 completed March 27, 2026, 7:10 a.m.
Created at: March 22, 2026, 4:31 p.m.