Triple

T7470980
Position Surface form Disambiguated ID Type / Status
Subject Marina Nikolayevna Prusakova E176501 entity
Predicate livedIn P75 FINISHED
Object New Orleans E3902 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: New Orleans | Statement: [Marina Nikolayevna Prusakova, livedIn, New Orleans]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: New Orleans
Context triple: [Marina Nikolayevna Prusakova, livedIn, New Orleans]
  • A. New Orleans chosen
    New Orleans is a historic port city in southeastern Louisiana known for its vibrant jazz music, Creole cuisine, and distinctive French and Spanish-influenced architecture.
  • B. City of New Orleans
    City of New Orleans is a famous long-distance passenger train that runs between Chicago and New Orleans and was popularized by the folk song of the same name.
  • C. Nola
    Nola is an ancient town in southern Italy, historically significant in Roman times and known as the place where Emperor Augustus died.
  • D. Baton Rouge, Louisiana
    Baton Rouge, Louisiana is the capital city of Louisiana, known for its role as a political, industrial, and cultural center along the Mississippi River.
  • E. Shreveport
    Shreveport is a major city in northwestern Louisiana known for its role as a regional commercial, cultural, and transportation hub.
  • 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_69c69f223fd88190b4c69b95d7cbeeda completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f4145d608190bd93239f04f7da41 completed March 27, 2026, 9:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69c83c625a388190bfe9237568ab2005 completed March 28, 2026, 8:38 p.m.
Created at: March 27, 2026, 3:41 p.m.