Triple

T14879169
Position Surface form Disambiguated ID Type / Status
Subject Kris Kamm E349948 entity
Predicate hasFamilyName P18 FINISHED
Object Kamm
Kamm is a surname most notably associated with American actor Kris Kamm, known for his roles in film and television in the late 20th century.
 E1125337 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: Kamm | Statement: [Kris Kamm, hasFamilyName, Kamm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kamm
Context triple: [Kris Kamm, hasFamilyName, Kamm]
  • A. Camm
    Camm is a surname most notably associated with Sir Sydney Camm, the influential British aircraft designer behind the Hawker Hurricane.
  • B. 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.
  • C. Couper
    Couper is a surname and variant spelling of Cooper, used by various individuals and families, particularly in English-speaking regions.
  • D. Kemeny
    Kemeny is a surname most notably associated with figures such as mathematician and computer scientist John G. Kemeny, co-developer of the BASIC programming language and former president of Dartmouth College.
  • E. Ackerman
    Ackerman is a surname of German and Jewish origin borne by various notable individuals across fields such as politics, arts, and academia.
  • 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: Kamm
Triple: [Kris Kamm, hasFamilyName, Kamm]
Generated description
Kamm is a surname most notably associated with American actor Kris Kamm, known for his roles in film and television in the late 20th century.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kamm
Target entity description: Kamm is a surname most notably associated with American actor Kris Kamm, known for his roles in film and television in the late 20th century.
  • A. Camm
    Camm is a surname most notably associated with Sir Sydney Camm, the influential British aircraft designer behind the Hawker Hurricane.
  • B. 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.
  • C. Couper
    Couper is a surname and variant spelling of Cooper, used by various individuals and families, particularly in English-speaking regions.
  • D. Kemeny
    Kemeny is a surname most notably associated with figures such as mathematician and computer scientist John G. Kemeny, co-developer of the BASIC programming language and former president of Dartmouth College.
  • E. Ackerman
    Ackerman is a surname of German and Jewish origin borne by various notable individuals across fields such as politics, arts, and academia.
  • 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_69d822ee4f408190b6ac3b2fa434f0df completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded5e622388190b2bf91cd10b9821d completed April 15, 2026, 12:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe6b5670108190b41ef95dc318be60 completed May 8, 2026, 11:01 p.m.
NEDg Description generation batch_69fe6d3aa2f08190b02c6157c03a2ba5 completed May 8, 2026, 11:09 p.m.
NED2 Entity disambiguation (via description) batch_69fe6db30e9c81908fbad7b932799a7a completed May 8, 2026, 11:11 p.m.
Created at: April 10, 2026, 1:55 a.m.