Triple

T19028693
Position Surface form Disambiguated ID Type / Status
Subject Fünke family E465677 entity
Predicate hasMember P10 FINISHED
Object Tobias Fünke 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: Tobias Fünke | Statement: [Fünke family, hasMember, Tobias Fünke]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tobias Fünke
Context triple: [Fünke family, hasMember, Tobias Fünke]
  • A. Tobias Fünke chosen
    Tobias Fünke is a socially awkward, aspiring actor and former analyst-therapist known for his oblivious behavior and unintentional double entendres in the television series "Arrested Development."
  • B. Markus Häußler
    Markus Häußler is a German local politician who serves as the mayor of the town of Munderkingen in Baden-Württemberg.
  • C. Markus Häußler
    Markus Häußler is a German local politician who serves as the mayor of the municipality of Illerkirchberg in Baden-Württemberg.
  • D. Tobias Kohn
    Tobias Kohn is a computer scientist and software developer known for his contributions to the Python language, including co-authoring PEP 622 on pattern matching.
  • E. Markus Förderer
    Markus Förderer is a German cinematographer known for his visually striking work on films such as Stonewall (2015), Independence Day: Resurgence, and I Origins.
  • 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_69d8dd0359648190bc2a9202c5cf29d2 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5d73ec9088190a98e214bd56e8622 completed April 20, 2026, 7:35 a.m.
Created at: April 10, 2026, 12:02 p.m.