Triple

T14677683
Position Surface form Disambiguated ID Type / Status
Subject Marianne Huber E344690 entity
Predicate nameInLatinAlphabet P22444 FINISHED
Object Marianne Huber E344690 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: Marianne Huber | Statement: [Marianne Huber, nameInLatinAlphabet, Marianne Huber]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marianne Huber
Context triple: [Marianne Huber, nameInLatinAlphabet, Marianne Huber]
  • A. Marianne Huber chosen
    Marianne Huber is a person notable enough to be recognized as a significant bearer of the surname Huber, though specific widely known public details about her are not clearly established.
  • B. Karin Huber
    Karin Huber is a person notable enough to be recognized as a significant bearer of the surname Huber.
  • C. Marianne Sägebrecht
    Marianne Sägebrecht is a German actress known for her distinctive character roles in films such as "Sugarbaby" and "Bagdad Café."
  • D. Johanna Hiedler
    Johanna Hiedler was an Austrian woman of the 19th century known primarily as the maternal grandmother of Adolf Hitler.
  • E. Marianne Willisch
    Marianne Willisch is an artist and designer associated with the New Bauhaus movement in Chicago.
  • 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_69d822e34b348190ada4d1cdb6c7c226 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb567c2b88190a9639e61b6fba7df completed April 14, 2026, 9:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe968124d481909dd89dd9282788b7 completed May 9, 2026, 2:05 a.m.
Created at: April 10, 2026, 1:27 a.m.