Triple

T20629610
Position Surface form Disambiguated ID Type / Status
Subject Thad Jones E506915 entity
Predicate laterResidence P4907 FINISHED
Object Copenhagen, Denmark NE NERFINISHED

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: Copenhagen, Denmark | Statement: [Thad Jones, laterResidence, Copenhagen, Denmark]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Copenhagen, Denmark
Context triple: [Thad Jones, laterResidence, Copenhagen, Denmark]
  • A. Copenhagen chosen
    Copenhagen is the capital and largest city of Denmark, known for its historic architecture, vibrant cultural scene, and high quality of life.
  • B. Copenhagen
    Copenhagen is a popular American smokeless tobacco (chewing tobacco/dip) brand known for its long history and strong presence in the U.S. market.
  • C. Frederiksberg, Denmark
    Frederiksberg, Denmark is an affluent, centrally located municipality within the Copenhagen urban area, known for its green parks, cultural institutions, and residential character.
  • D. UN City Copenhagen
    UN City Copenhagen is a modern, sustainable office complex in Denmark that serves as the Nordic headquarters for multiple United Nations agencies.
  • E. Døstrup, Denmark
    Døstrup, Denmark is a small Danish village best known as the birthplace of cartoonist Kurt Westergaard.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4bd4a0081908d4e97a590a33fb2 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6abe771e88190a48471bf83b4804d completed April 20, 2026, 10:42 p.m.
Created at: April 16, 2026, 11:42 a.m.