Triple

T6800523
Position Surface form Disambiguated ID Type / Status
Subject Der große Bellheim E156173 entity
Predicate stars P1956 FINISHED
Object Hans Korte
Hans Korte was a German actor known for his extensive work in film, television, and theater, often portraying authoritative and distinguished characters.
E620942 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: Hans Korte | Statement: [Der große Bellheim, stars, Hans Korte]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hans Korte
Context triple: [Der große Bellheim, stars, Hans Korte]
  • A. Rolf Lohse
    Rolf Lohse is a German former sprint canoeist who competed internationally in the 1970s.
  • B. Heinz Behrens
    Heinz Behrens was a German actor best known for his roles in East German film and television productions.
  • C. Rudolf Schröder
    Rudolf Schröder is a notable individual who bears the surname Schröder, recognized for his significance among people with that name.
  • D. Hans Albers
    Hans Albers was a celebrated German film and stage actor and singer, best known as one of the biggest stars of German cinema in the 1930s and 1940s.
  • E. René Mayer
    René Mayer was a French politician who served as Prime Minister of France and later became a leading figure in early European integration efforts.
  • 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: Hans Korte
Triple: [Der große Bellheim, stars, Hans Korte]
Generated description
Hans Korte was a German actor known for his extensive work in film, television, and theater, often portraying authoritative and distinguished characters.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Hans Korte
Target entity description: Hans Korte was a German actor known for his extensive work in film, television, and theater, often portraying authoritative and distinguished characters.
  • A. Rolf Lohse
    Rolf Lohse is a German former sprint canoeist who competed internationally in the 1970s.
  • B. Heinz Behrens
    Heinz Behrens was a German actor best known for his roles in East German film and television productions.
  • C. Rudolf Schröder
    Rudolf Schröder is a notable individual who bears the surname Schröder, recognized for his significance among people with that name.
  • D. Hans Albers
    Hans Albers was a celebrated German film and stage actor and singer, best known as one of the biggest stars of German cinema in the 1930s and 1940s.
  • E. René Mayer
    René Mayer was a French politician who served as Prime Minister of France and later became a leading figure in early European integration efforts.
  • 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_69c6881844448190a65822d9b39d7f88 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d2e457408190a0ad9b0c48d8147c completed March 27, 2026, 6:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69c723d211508190a31747b2d67ed42e completed March 28, 2026, 12:41 a.m.
NEDg Description generation batch_69c7244c3dcc8190852a26404354e63d completed March 28, 2026, 12:43 a.m.
NED2 Entity disambiguation (via description) batch_69c724d8f3bc819090cf65d6ae0ba1ed completed March 28, 2026, 12:46 a.m.
Created at: March 27, 2026, 2:15 p.m.