Triple

T15393166
Position Surface form Disambiguated ID Type / Status
Subject To Catch a Killer E368099 entity
Predicate editor P1954 FINISHED
Object Ralph Brunjes
Ralph Brunjes is a film editor known for his work on the crime drama "To Catch a Killer."
 E1156025 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: Ralph Brunjes | Statement: [To Catch a Killer, editor, Ralph Brunjes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ralph Brunjes
Context triple: [To Catch a Killer, editor, Ralph Brunjes]
  • A. George Borchers
    George Borchers was a 19th-century American professional baseball pitcher who played in Major League Baseball.
  • B. Ralph Fulton
    Ralph Fulton is a British video game developer best known as the creative leader behind the Forza Horizon series and a founding figure of Playground Games.
  • C. Ralph Greaves
    Ralph Greaves was a British composer and arranger best known for his orchestral work and collaborations, including his contributions to English classical repertoire.
  • D. Joseph Henabery
    Joseph Henabery was an American film director and actor of the silent era, known for his work with early Hollywood studios and contributions to pioneering feature films.
  • E. Victor Maddern
    Victor Maddern was a British character actor known for his prolific film and television work from the 1950s onward, often portraying tough or military types in comedies and dramas.
  • 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: Ralph Brunjes
Triple: [To Catch a Killer, editor, Ralph Brunjes]
Generated description
Ralph Brunjes is a film editor known for his work on the crime drama "To Catch a Killer."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ralph Brunjes
Target entity description: Ralph Brunjes is a film editor known for his work on the crime drama "To Catch a Killer."
  • A. George Borchers
    George Borchers was a 19th-century American professional baseball pitcher who played in Major League Baseball.
  • B. Ralph Fulton
    Ralph Fulton is a British video game developer best known as the creative leader behind the Forza Horizon series and a founding figure of Playground Games.
  • C. Ralph Greaves
    Ralph Greaves was a British composer and arranger best known for his orchestral work and collaborations, including his contributions to English classical repertoire.
  • D. Joseph Henabery
    Joseph Henabery was an American film director and actor of the silent era, known for his work with early Hollywood studios and contributions to pioneering feature films.
  • E. Victor Maddern
    Victor Maddern was a British character actor known for his prolific film and television work from the 1950s onward, often portraying tough or military types in comedies and dramas.
  • 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_69d85a1551a08190ba2caea7cd51c639 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e7838b48190862b43c6c8620692 completed April 16, 2026, 1:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff1a6f76d48190ac6032c55adbc4af completed May 9, 2026, 11:28 a.m.
NEDg Description generation batch_69ff1b7559448190b58c6119916774c8 completed May 9, 2026, 11:33 a.m.
NED2 Entity disambiguation (via description) batch_69ff1bdb39b481908f0b1df595837bc4 completed May 9, 2026, 11:34 a.m.
Created at: April 10, 2026, 3:19 a.m.