Triple

T21656291
Position Surface form Disambiguated ID Type / Status
Subject Maria Feodorovna E534474 entity
Predicate residence P75 FINISHED
Object Hvidøre (near 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: Hvidøre (near Copenhagen) | Statement: [Maria Feodorovna, residence, Hvidøre (near Copenhagen)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hvidøre (near Copenhagen)
Context triple: [Maria Feodorovna, residence, Hvidøre (near Copenhagen)]
  • A. Døstrup, Denmark
    Døstrup, Denmark is a small Danish village best known as the birthplace of cartoonist Kurt Westergaard.
  • B. Hillerød
    Hillerød is a Danish town on the island of Zealand, known for the historic Frederiksborg Castle and its role as a regional administrative and cultural center.
  • C. Billund, Denmark
    Billund, Denmark is a small Danish town best known as the birthplace of LEGO and home to the original LEGOLAND theme park.
  • D. Hvidovre chosen
    Hvidovre is a suburban municipality in the Capital Region of Denmark, located just southwest of central Copenhagen.
  • E. Rødovre
    Rødovre is a suburban municipality in the Capital Region of Denmark, located just west of central Copenhagen.
  • 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_69e0c467e1f48190af2650b19175abc4 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69ef59185b208190a2cf4b8f54a2c231 completed April 27, 2026, 12:39 p.m.
Created at: April 16, 2026, 6:36 p.m.