Triple

T10738619
Position Surface form Disambiguated ID Type / Status
Subject Männerpension E253260 entity
Predicate hasCastMember P2308 FINISHED
Object Markus Knüfken
Markus Knüfken is a German actor known for his roles in film and television since the 1990s.
 E887173 NE FINISHED

How this triple was built (4 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: Markus Knüfken | Statement: [Männerpension, hasCastMember, Markus Knüfken]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Markus Knüfken
Context triple: [Männerpension, hasCastMember, Markus Knüfken]
  • A. Markus Vogt
    Markus Vogt is an architect known for his work on the design of the Bundesplatz in Switzerland.
  • B. Hannes Messemer
    Hannes Messemer was a German actor best known for his roles in postwar European cinema, particularly in war and drama films.
  • C. Markus Häußler
    Markus Häußler is a German local politician who serves as the mayor of the municipality of Illerkirchberg in Baden-Württemberg.
  • D. Markus Häußler
    Markus Häußler is a German local politician who serves as the mayor of the town of Munderkingen in Baden-Württemberg.
  • E. Philipp Demandt
    Philipp Demandt is a German art historian and museum director known for leading major cultural institutions such as the Städel Museum and the Liebieghaus Skulpturensammlung in Frankfurt.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Markus Knüfken
Triple: [Männerpension, hasCastMember, Markus Knüfken]
Generated description
Markus Knüfken is a German actor known for his roles in film and television since the 1990s.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Markus Knüfken
Target entity description: Markus Knüfken is a German actor known for his roles in film and television since the 1990s.
  • A. Markus Vogt
    Markus Vogt is an architect known for his work on the design of the Bundesplatz in Switzerland.
  • B. Hannes Messemer
    Hannes Messemer was a German actor best known for his roles in postwar European cinema, particularly in war and drama films.
  • C. Markus Häußler
    Markus Häußler is a German local politician who serves as the mayor of the municipality of Illerkirchberg in Baden-Württemberg.
  • D. Markus Häußler
    Markus Häußler is a German local politician who serves as the mayor of the town of Munderkingen in Baden-Württemberg.
  • E. Philipp Demandt
    Philipp Demandt is a German art historian and museum director known for leading major cultural institutions such as the Städel Museum and the Liebieghaus Skulpturensammlung in Frankfurt.
  • F. None of above. chosen

Provenance (5 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_69d6aa5e51e8819095f06881cecf152e completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d710424d8c81908ee9b59d622f2af5 completed April 9, 2026, 2:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69de84932abc8190907c32720e35442e completed April 14, 2026, 6:16 p.m.
NEDg Description generation batch_69de8954500c81909b57c4f8007959aa completed April 14, 2026, 6:37 p.m.
NED2 Entity disambiguation (via description) batch_69de8f38e3048190b1acc81bb56fe165 completed April 14, 2026, 7:02 p.m.
Created at: April 8, 2026, 9:14 p.m.