Triple

T16621244
Position Surface form Disambiguated ID Type / Status
Subject Sigvard Bernadotte E403833 entity
Predicate workLocation P7 FINISHED
Object Denmark E5474 NE FINISHED

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: [Sigvard Bernadotte, workLocation, Denmark]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Denmark
Context triple: [Sigvard Bernadotte, workLocation, 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 (3 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_69d883897eb481909eaaa088ba9918d9 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3754d6b4c81908eab5210c386ea15 completed April 18, 2026, 12:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a007da6d6f08190a8524b1c955b7c2e completed May 10, 2026, 12:44 p.m.
Created at: April 10, 2026, 5:17 a.m.