Triple

T19619533
Position Surface form Disambiguated ID Type / Status
Subject Lee Cullen E470960 entity
Predicate hasAlly P600 FINISHED
Object John Kruger
John Kruger is the U.S. Marshal and witness protection specialist portrayed by Arnold Schwarzenegger in the 1996 action film "Eraser."
 E439251 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: John Kruger | Statement: [Lee Cullen, hasAlly, John Kruger]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: John Kruger
Context triple: [Lee Cullen, hasAlly, John Kruger]
  • A. John Kruger
    John Kruger is the tough, resourceful U.S. Marshal protagonist in the 1996 action film "Eraser," portrayed by Arnold Schwarzenegger.
  • B. Michael Krueger
    Michael Krueger is a notable individual distinguished enough to be recognized as a prominent bearer of the surname Krueger.
  • C. David Krueger
    David Krueger is an AI researcher and entrepreneur best known as a co-founder of the safety-focused artificial intelligence company Anthropic.
  • D. Ken Krueger
    Ken Krueger was an American publisher and bookseller best known as a pioneering figure in comics fandom and a key organizer in the early development of major comic conventions.
  • E. Ehren Kruger
    Ehren Kruger is an American screenwriter and film producer known for writing several entries in the Transformers franchise and other major Hollywood films.
  • 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: John Kruger
Triple: [Lee Cullen, hasAlly, John Kruger]
Generated description
John Kruger is the U.S. Marshal and witness protection specialist portrayed by Arnold Schwarzenegger in the 1996 action film "Eraser."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: John Kruger
Target entity description: John Kruger is the U.S. Marshal and witness protection specialist portrayed by Arnold Schwarzenegger in the 1996 action film "Eraser."
  • A. John Kruger chosen
    John Kruger is the tough, resourceful U.S. Marshal protagonist in the 1996 action film "Eraser," portrayed by Arnold Schwarzenegger.
  • B. Michael Krueger
    Michael Krueger is a notable individual distinguished enough to be recognized as a prominent bearer of the surname Krueger.
  • C. David Krueger
    David Krueger is an AI researcher and entrepreneur best known as a co-founder of the safety-focused artificial intelligence company Anthropic.
  • D. Ken Krueger
    Ken Krueger was an American publisher and bookseller best known as a pioneering figure in comics fandom and a key organizer in the early development of major comic conventions.
  • E. Ehren Kruger
    Ehren Kruger is an American screenwriter and film producer known for writing several entries in the Transformers franchise and other major Hollywood films.
  • F. None of above.

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_69d8e510fa248190b7afb274a1d4cf73 completed April 10, 2026, 11:54 a.m.
NER Named-entity recognition batch_69e640e4570c81909fc4f9b871346337 completed April 20, 2026, 3:06 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0787afa9ec81908f7471b2e77841a7 completed May 15, 2026, 8:53 p.m.
NEDg Description generation batch_6a0791263314819093b6444c1b8406d5 completed May 15, 2026, 9:33 p.m.
NED2 Entity disambiguation (via description) batch_6a0791f98d94819093e70884ee3b6c5c completed May 15, 2026, 9:36 p.m.
Created at: April 10, 2026, 1:43 p.m.