Triple

T15773633
Position Surface form Disambiguated ID Type / Status
Subject Verband der Deutschen Buchdrucker E382425 entity
Predicate hasMember P10 FINISHED
Object Setzer
Setzer is a German term for a typesetter, a skilled printing trade professional responsible for arranging text for printing.
 E1175612 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: Setzer | Statement: [Verband der Deutschen Buchdrucker, hasMember, Setzer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Setzer
Context triple: [Verband der Deutschen Buchdrucker, hasMember, Setzer]
  • A. Kleiser
    Kleiser is a surname most notably associated with American film director Randal Kleiser, known for directing the musical romantic comedy "Grease."
  • B. Schnetzer
    Schnetzer is a surname most notably associated with American actor Ben Schnetzer, known for his roles in films such as "Pride" and "The Book Thief."
  • C. Stottlemeyer
    Stottlemeyer is the surname of Captain Leland Stottlemeyer, a central police character from the television series "Monk."
  • D. Loerzer
    Loerzer is the surname of Bruno Loerzer, a notable German First World War flying ace and later Luftwaffe general.
  • E. Oberholtzer
    Oberholtzer is a German-origin surname, often associated with Mennonite and Amish families, that serves as a variant of the Overholt family name.
  • 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: Setzer
Triple: [Verband der Deutschen Buchdrucker, hasMember, Setzer]
Generated description
Setzer is a German term for a typesetter, a skilled printing trade professional responsible for arranging text for printing.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Setzer
Target entity description: Setzer is a German term for a typesetter, a skilled printing trade professional responsible for arranging text for printing.
  • A. Kleiser
    Kleiser is a surname most notably associated with American film director Randal Kleiser, known for directing the musical romantic comedy "Grease."
  • B. Schnetzer
    Schnetzer is a surname most notably associated with American actor Ben Schnetzer, known for his roles in films such as "Pride" and "The Book Thief."
  • C. Stottlemeyer
    Stottlemeyer is the surname of Captain Leland Stottlemeyer, a central police character from the television series "Monk."
  • D. Loerzer
    Loerzer is the surname of Bruno Loerzer, a notable German First World War flying ace and later Luftwaffe general.
  • E. Oberholtzer
    Oberholtzer is a German-origin surname, often associated with Mennonite and Amish families, that serves as a variant of the Overholt family name.
  • 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_69d86da09a10819082fe9797b23e4664 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e051976d248190adddd3db9f758e22 completed April 16, 2026, 3:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff877e67b881908a67b9acc79d998f completed May 9, 2026, 7:14 p.m.
NEDg Description generation batch_69ff88358e408190a8d7424fc495d4fa completed May 9, 2026, 7:17 p.m.
NED2 Entity disambiguation (via description) batch_69ff88f192a08190acbc2c3fc98c65c8 completed May 9, 2026, 7:20 p.m.
Created at: April 10, 2026, 4:47 a.m.