Triple

T16324980
Position Surface form Disambiguated ID Type / Status
Subject Frederica Louise Wilhelmina of Orange-Nassau E396387 entity
Predicate givenName P17 FINISHED
Object Wilhelmina E347442 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: Wilhelmina | Statement: [Frederica Louise Wilhelmina of Orange-Nassau, givenName, Wilhelmina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wilhelmina
Context triple: [Frederica Louise Wilhelmina of Orange-Nassau, givenName, Wilhelmina]
  • A. Wilhelmina chosen
    Wilhelmina was a Prussian princess of the House of Hohenzollern who became Princess of Orange through marriage and played a significant political role in the Dutch Republic in the late 18th century.
  • B. Wilhelmina
    Wilhelmina is the given name of Lady Catherine Lucy Wilhelmina Stanhope, a 19th-century British aristocrat and political hostess.
  • C. Wilhelmina
    Wilhelmina is a feminine given name of Germanic origin historically borne by European royalty and nobility.
  • D. Luise
    Luise is a given name, primarily used in German-speaking countries, that corresponds to the English and French name Louise.
  • E. Wilhelmina Behmenburg
    Wilhelmina Behmenburg, better known as Wilhelmina Cooper, was a prominent Dutch-born fashion model and influential modeling agency founder in the mid-20th century.
  • 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_69d87f255b788190a400eba031dd85d8 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e296b9dcb88190beb0ca2206729175 completed April 17, 2026, 8:23 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00260ca9f08190aa95560fea482dd4 completed May 10, 2026, 6:30 a.m.
Created at: April 10, 2026, 5:06 a.m.