Triple

T2071773
Position Surface form Disambiguated ID Type / Status
Subject Luisa E44829 entity
Predicate relatedName P3889 FINISHED
Object Luise E39188 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: Luise | Statement: [Luisa, relatedName, Luise]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Luise
Context triple: [Luisa, relatedName, Luise]
  • A. Luise chosen
    Luise is a given name, primarily used in German-speaking countries, that corresponds to the English and French name Louise.
  • B. 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.
  • C. Therese of Saxe-Hildburghausen
    Therese of Saxe-Hildburghausen was a Bavarian queen consort whose marriage to Crown Prince Ludwig I of Bavaria is famously commemorated by the annual Oktoberfest in Munich.
  • D. Ludovika
    Ludovika is a feminine given name, used as a variant spelling of Ludovica in various European languages.
  • E. Dagmar
    Dagmar is a feminine given name of Germanic origin, historically associated with European nobility and still used in various countries today.
  • 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_69a88916c2b48190a5ca2e9b12cad3ed completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abba0d20bc8190b19a32157f8b1607 completed March 7, 2026, 5:39 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae7eee9bd88190a46e34030939a568 completed March 9, 2026, 8:03 a.m.
Created at: March 4, 2026, 7:41 p.m.