Triple

T9280625
Position Surface form Disambiguated ID Type / Status
Subject Jordan Klepper E223056 entity
Predicate familyName P18 FINISHED
Object Klepper
Klepper is the surname of American comedian and television host Jordan Klepper, known for his work on political satire programs such as The Daily Show.
 E789293 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: Klepper | Statement: [Jordan Klepper, familyName, Klepper]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Klepper
Context triple: [Jordan Klepper, familyName, Klepper]
  • A. Oberholtzer
    Oberholtzer is a German-origin surname, often associated with Mennonite and Amish families, that serves as a variant of the Overholt family name.
  • B. Mennekes
    Mennekes is a German electrical engineering company best known in e-mobility for developing the widely adopted Type 2 AC charging connector for electric vehicles.
  • C. Keeler
    Keeler is a surname most notably associated with James Edward Keeler, an American astronomer known for his pioneering work on Saturn's rings and spectroscopy.
  • D. Hufstedler
    Hufstedler is the surname of Shirley Hufstedler, a prominent American judge and the first U.S. Secretary of Education.
  • E. Dellner
    Dellner is a company specializing in railway coupling and connection systems used on modern passenger and freight trains worldwide.
  • 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: Klepper
Triple: [Jordan Klepper, familyName, Klepper]
Generated description
Klepper is the surname of American comedian and television host Jordan Klepper, known for his work on political satire programs such as The Daily Show.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Klepper
Target entity description: Klepper is the surname of American comedian and television host Jordan Klepper, known for his work on political satire programs such as The Daily Show.
  • A. Oberholtzer
    Oberholtzer is a German-origin surname, often associated with Mennonite and Amish families, that serves as a variant of the Overholt family name.
  • B. Mennekes
    Mennekes is a German electrical engineering company best known in e-mobility for developing the widely adopted Type 2 AC charging connector for electric vehicles.
  • C. Keeler
    Keeler is a surname most notably associated with James Edward Keeler, an American astronomer known for his pioneering work on Saturn's rings and spectroscopy.
  • D. Hufstedler
    Hufstedler is the surname of Shirley Hufstedler, a prominent American judge and the first U.S. Secretary of Education.
  • E. Dellner
    Dellner is a company specializing in railway coupling and connection systems used on modern passenger and freight trains worldwide.
  • 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_69ca842123588190b3f2e1a69037d141 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd07cd9a1c8190af0521baa428ce10 completed April 1, 2026, 11:55 a.m.
NED1 Entity disambiguation (via context triple) batch_69d0b1fef1508190a9bf1a55dd39c0ac completed April 4, 2026, 6:38 a.m.
NEDg Description generation batch_69d0b2e45674819091c7e1102c8844a5 completed April 4, 2026, 6:42 a.m.
NED2 Entity disambiguation (via description) batch_69d0b36590088190a92401d5f6c4c947 completed April 4, 2026, 6:44 a.m.
Created at: March 30, 2026, 7:34 p.m.