Triple

T9949306
Position Surface form Disambiguated ID Type / Status
Subject Butz E195286 entity
Predicate hasNotableBearer P458 FINISHED
Object Christian Butz
Christian Butz is a person notable enough to be recognized as a bearer of the surname Butz, though specific widely known public details about him are not clearly established.
 E879667 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: Christian Butz | Statement: [Butz, hasNotableBearer, Christian Butz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Christian Butz
Context triple: [Butz, hasNotableBearer, Christian Butz]
  • A. Paul Butz
    Paul Butz is an individual notable enough to be specifically cited as a bearer of the surname Butz.
  • B. Peter Butz
    Peter Butz is an individual notable enough to be recognized as a prominent bearer of the surname Butz.
  • C. Charles Butz
    Charles Butz is an individual notable enough to be recognized as a bearer of the surname Butz, though specific widely known achievements or roles are not clearly documented.
  • D. Robert Butz
    Robert Butz is an individual notable enough to be specifically recorded as a bearer of the surname Butz.
  • E. Martin Butz
    Martin Butz is a researcher known for his work in cognitive science, machine learning, and evolutionary computation, particularly in the development of learning classifier systems.
  • 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: Christian Butz
Triple: [Butz, hasNotableBearer, Christian Butz]
Generated description
Christian Butz is a person notable enough to be recognized as a bearer of the surname Butz, though specific widely known public details about him are not clearly established.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Christian Butz
Target entity description: Christian Butz is a person notable enough to be recognized as a bearer of the surname Butz, though specific widely known public details about him are not clearly established.
  • A. Paul Butz
    Paul Butz is an individual notable enough to be specifically cited as a bearer of the surname Butz.
  • B. Peter Butz
    Peter Butz is an individual notable enough to be recognized as a prominent bearer of the surname Butz.
  • C. Charles Butz
    Charles Butz is an individual notable enough to be recognized as a bearer of the surname Butz, though specific widely known achievements or roles are not clearly documented.
  • D. Robert Butz
    Robert Butz is an individual notable enough to be specifically recorded as a bearer of the surname Butz.
  • E. Martin Butz
    Martin Butz is a researcher known for his work in cognitive science, machine learning, and evolutionary computation, particularly in the development of learning classifier systems.
  • 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_69ca82e96a108190932bd1fc4acd73a0 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdb65a4e6c8190968192a24aad1b7d completed April 2, 2026, 12:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69d98801deb8819092a45193078f09b4 completed April 10, 2026, 11:30 p.m.
NEDg Description generation batch_69d98ae8403c81908a229aa06bd0388a completed April 10, 2026, 11:42 p.m.
NED2 Entity disambiguation (via description) batch_69d98ce9ba0c8190a7c62fa670e23705 completed April 10, 2026, 11:51 p.m.
Created at: March 30, 2026, 8:45 p.m.