Triple

T23134220
Position Surface form Disambiguated ID Type / Status
Subject Allerød Municipality E577263 entity
Predicate countrySubdivision P766 FINISHED
Object 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: Denmark | Statement: [Allerød Municipality, countrySubdivision, Denmark]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Denmark
Context triple: [Allerød Municipality, countrySubdivision, Denmark]
  • A. Denmark chosen
    Denmark is a Nordic country in Northern Europe known for its high standard of living, strong welfare state, and role as a founding member of NATO and the United Nations.
  • B. Dania
    Dania was the original name of the South Florida city now known as Dania Beach, historically recognized as one of the region’s earliest incorporated communities.
  • C. Daens
    Daens is a 1992 Belgian historical drama film about a Catholic priest who fights social injustice and exploitation of workers in late 19th-century Belgium.
  • D. Sweden and Denmark
    Sweden and Denmark are neighboring Scandinavian countries in Northern Europe, separated by the Øresund Strait and closely linked through extensive cultural, economic, and transport connections.
  • E. Norway
    Norway is a Nordic country in Northern Europe known for its high standard of living, extensive welfare state, and dramatic natural landscapes of fjords, mountains, and coastline.
  • 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_69e245f7b0e481909c473ff4e6a54e2c completed April 17, 2026, 2:38 p.m.
NER Named-entity recognition batch_69f18e8ab9b08190969984f8c4494b0f completed April 29, 2026, 4:52 a.m.
Created at: April 17, 2026, 4 p.m.