Triple

T22203049
Position Surface form Disambiguated ID Type / Status
Subject Hillerødmotorvejen E548728 entity
Predicate terminus P388 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: [Hillerødmotorvejen, terminus, Copenhagen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Copenhagen
Context triple: [Hillerødmotorvejen, terminus, 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_69e11e3ecc7c8190b5f94cd8f42e9d37 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f12b24c6fc81909e6ae62564846bd1 completed April 28, 2026, 9:48 p.m.
Created at: April 16, 2026, 8:36 p.m.