Triple

T17988237
Position Surface form Disambiguated ID Type / Status
Subject Dagmara E430296 entity
Predicate relatedName P3889 FINISHED
Object Dagmar 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: Dagmar | Statement: [Dagmara, relatedName, Dagmar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dagmar
Context triple: [Dagmara, relatedName, Dagmar]
  • A. Dagmar chosen
    Dagmar is a feminine given name of Germanic origin, historically associated with European nobility and still used in various countries today.
  • B. Hedvig
    Hedvig is a Scandinavian female given name, historically borne by several notable women in Swedish and broader Nordic royalty and nobility.
  • C. Florence Dagmar
    Florence Dagmar was an early 20th-century American silent film actress known for her roles in dramas of the 1910s.
  • D. Ulrike
    Ulrike is a German given name, typically feminine, derived from the name Ulrich and associated with German-speaking countries.
  • E. Dagmar of Denmark
    Dagmar of Denmark, later known as Empress Maria Feodorovna, was a Danish princess who became Empress consort of Russia as the wife of Tsar Alexander III and mother of the last Russian tsar, Nicholas II.
  • 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_69d8b90364248190a37381adea932f42 completed April 10, 2026, 8:46 a.m.
NER Named-entity recognition batch_69e4b29d3ad4819096c2600aa2a99f21 completed April 19, 2026, 10:46 a.m.
Created at: April 10, 2026, 10:23 a.m.