Triple

T13342185
Position Surface form Disambiguated ID Type / Status
Subject Secession Building E317852 entity
Predicate hasCity P316 FINISHED
Object Vienna E7023 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: Vienna | Statement: [Secession Building, hasCity, Vienna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vienna
Context triple: [Secession Building, hasCity, Vienna]
  • A. Vienna chosen
    Vienna is the capital city of Austria, renowned for its rich imperial history, classical music heritage, and vibrant cultural and intellectual life.
  • B. Vienna
    Vienna is a suburban town in Fairfax County, Virginia, known for its residential neighborhoods, proximity to Washington, D.C., and access to the Washington Metro via the nearby Vienna/Fairfax–GMU station.
  • C. Vienna
    Vienna is a small town in Dane County, Wisconsin, known for its rural character and proximity to the Madison metropolitan area.
  • D. Vienna
    Vienna is the strong-willed saloon owner and central female protagonist in the 1954 Western film "Johnny Guitar."
  • E. Wien
    Wien is a German surname most notably borne by physicist Wilhelm Wien, known for his work on blackbody radiation and Wien's displacement law.
  • 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_69d806b5a3c08190b42c267fb092f98a completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d99d0379d481909a50fff31b19fed1 completed April 11, 2026, 12:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7396e6cf881908b4cc3836501ed08 completed May 3, 2026, 12:02 p.m.
Created at: April 9, 2026, 9:31 p.m.