Triple

T17224725
Position Surface form Disambiguated ID Type / Status
Subject Zakharia Paliashvili E418083 entity
Predicate countryOfCitizenship P2 FINISHED
Object Georgia E28340 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: Georgia | Statement: [Zakharia Paliashvili, countryOfCitizenship, Georgia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Georgia
Context triple: [Zakharia Paliashvili, countryOfCitizenship, Georgia]
  • A. Georgia
    Georgia is a southeastern U.S. state known for its diverse landscapes, historic cities like Atlanta and Savannah, and significant roles in both the Civil War and the civil rights movement.
  • B. Georgia chosen
    Georgia is a country at the crossroads of Eastern Europe and Western Asia, known for its ancient culture, mountainous landscapes, and historic role along the Silk Road.
  • C. Georgia
    Georgia is a popular Japanese canned coffee brand produced by The Coca-Cola Company, known for its wide variety of ready-to-drink coffee beverages.
  • D. Georgia
    Georgia is a character from the musical and film "Burlesque," known for her role as one of the performers in the nightclub where the story unfolds.
  • E. Georgia
    Georgia is a 1995 American drama film starring Jennifer Jason Leigh as a struggling singer overshadowed by her more successful sister.
  • 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_69d886d779488190b131369541c04e7d completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e42de0a8dc819093d9c8fb4f80342c completed April 19, 2026, 1:20 a.m.
NED1 Entity disambiguation (via context triple) batch_6a016745f32c81909499f71920e8babe completed May 11, 2026, 5:21 a.m.
Created at: April 10, 2026, 5:38 a.m.