Triple

T19831216
Position Surface form Disambiguated ID Type / Status
Subject John Louis Emil Dreyer E476464 entity
Predicate birthPlace P1 FINISHED
Object Copenhagen 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 | Statement: [John Louis Emil Dreyer, birthPlace, Copenhagen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Copenhagen
Context triple: [John Louis Emil Dreyer, birthPlace, Copenhagen]
  • 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. Odense
    Odense is a historic Danish city on the island of Funen, best known as the birthplace of fairy-tale author Hans Christian Andersen and a cultural hub with museums, festivals, and a vibrant literary heritage.
  • D. Hankø
    Hankø is a small Norwegian island and resort area known for its sailing, summer tourism, and scenic coastal landscapes.
  • E. Helsinge
    Helsinge is a town in North Zealand, Denmark, known as a local commercial and transport hub connected by rail to nearby cities including Hillerød.
  • 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_69d8e51c7c188190b926f3a2a7b5f881 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e656ce68b48190aa25b29d0b6ea021 completed April 20, 2026, 4:39 p.m.
Created at: April 10, 2026, 1:50 p.m.