Triple

T11076972
Position Surface form Disambiguated ID Type / Status
Subject Mina Harker E261892 entity
Predicate givenName P17 FINISHED
Object Wilhelmina E649686 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: [Mina Harker, givenName, Wilhelmina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wilhelmina
Context triple: [Mina Harker, givenName, Wilhelmina]
  • A. Wilhelmina
    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 chosen
    Wilhelmina is the given name of Lady Catherine Lucy Wilhelmina Stanhope, a 19th-century British aristocrat and political hostess.
  • C. Luise
    Luise is a given name, primarily used in German-speaking countries, that corresponds to the English and French name Louise.
  • D. Maria Christina
    Maria Christina, known as Princess Christina of the Netherlands, was a Dutch royal and youngest daughter of Queen Juliana and Prince Bernhard who became known for her work as a singer and music educator.
  • E. Maria Christina
    Maria Christina is a feminine given name of Latin origin, historically borne by various European noblewomen and royals.
  • 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_69d6aa9983c08190b0ef61603b69feac completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d7999407288190a901d4a2427a2102 completed April 9, 2026, 12:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3c8cc77988190aad54f56dbd0f8cf completed April 18, 2026, 6:09 p.m.
Created at: April 8, 2026, 9:27 p.m.