Triple

T2853587
Position Surface form Disambiguated ID Type / Status
Subject Tvishi E63147 entity
Predicate countryOfOrigin P26 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: [Tvishi, countryOfOrigin, Georgia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Georgia
Context triple: [Tvishi, countryOfOrigin, Georgia]
  • A. 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.
  • B. 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.
  • C. 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.
  • D. Carolinas
    The Carolinas are a region of the southeastern United States comprising the states of North Carolina and South Carolina.
  • E. South Carolina
    South Carolina is a southeastern U.S. state known for its Atlantic coastline, historic cities like Charleston, and significant role in early American and Civil War history.
  • 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_69ab4c407c408190857d25e027155ce9 completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abdf5f21348190a574fa86bc71c76f completed March 7, 2026, 8:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69b1de83d3008190bfb483e0c1e75700 completed March 11, 2026, 9:28 p.m.
Created at: March 6, 2026, 10:02 p.m.