Triple

T19203610
Position Surface form Disambiguated ID Type / Status
Subject Kenneth Schnitzer E480172 entity
Predicate hasFamilyName P18 FINISHED
Object Schnitzer
Schnitzer is a German-language surname borne by various individuals and families of German or Central European origin.
 E1365227 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: Schnitzer | Statement: [Kenneth Schnitzer, hasFamilyName, Schnitzer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Schnitzer
Context triple: [Kenneth Schnitzer, hasFamilyName, Schnitzer]
  • A. Seiberling
    Seiberling is a surname most notably associated with American industrialist Frank Seiberling, co-founder of the Goodyear Tire & Rubber Company.
  • 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. Stöcker
    Stöcker is a German surname that is a variant of the name Stoker.
  • D. Trumbauer
    Trumbauer is a surname most notably associated with American architect Horace Trumbauer, known for his grand Gilded Age mansions and institutional buildings.
  • E. Schmitz
    Schmitz is one of the two manipulative arsonists who infiltrate the bourgeois household in Max Frisch’s play "Biedermann und die Brandstifter."
  • 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: Schnitzer
Triple: [Kenneth Schnitzer, hasFamilyName, Schnitzer]
Generated description
Schnitzer is a German-language surname borne by various individuals and families of German or Central European origin.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Schnitzer
Target entity description: Schnitzer is a German-language surname borne by various individuals and families of German or Central European origin.
  • A. Seiberling
    Seiberling is a surname most notably associated with American industrialist Frank Seiberling, co-founder of the Goodyear Tire & Rubber Company.
  • 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. Stöcker
    Stöcker is a German surname that is a variant of the name Stoker.
  • D. Trumbauer
    Trumbauer is a surname most notably associated with American architect Horace Trumbauer, known for his grand Gilded Age mansions and institutional buildings.
  • E. Schmitz
    Schmitz is one of the two manipulative arsonists who infiltrate the bourgeois household in Max Frisch’s play "Biedermann und die Brandstifter."
  • 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_69d8e8cb8c348190b52075823911c869 completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e5f99a571c8190a1d53eb1994e0058 completed April 20, 2026, 10:02 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0700de73948190a22ce83fa351f099 completed May 15, 2026, 11:17 a.m.
NEDg Description generation batch_6a07028a76ec8190a73f5ad24d380855 completed May 15, 2026, 11:24 a.m.
NED2 Entity disambiguation (via description) batch_6a0703248b748190b683455342da7238 completed May 15, 2026, 11:27 a.m.
Created at: April 10, 2026, 1:15 p.m.