Triple

T11994779
Position Surface form Disambiguated ID Type / Status
Subject The Ladies Man E285500 entity
Predicate editedBy P1954 FINISHED
Object Stanley E. Johnson
Stanley E. Johnson is a film editor known for his work on the movie "The Ladies Man."
 E1361808 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: Stanley E. Johnson | Statement: [The Ladies Man, editedBy, Stanley E. Johnson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Stanley E. Johnson
Context triple: [The Ladies Man, editedBy, Stanley E. Johnson]
  • A. Stanley E. Johnson
    Stanley E. Johnson is an editor best known for his work on the classic American children's novel "Old Yeller."
  • B. Stanley E. Johnson
    Stanley E. Johnson is a film editor known for his work on movies such as the comedy feature "The Bellboy."
  • C. Ronald E. Yates
    Ronald E. Yates is an American author and former journalist known for his work as a foreign correspondent and as a professor and dean at the University of Illinois’ College of Media.
  • D. Ronald L. Vaughn
    Ronald L. Vaughn is an American academic administrator best known for leading the University of Tampa through significant growth and development as its long-serving president.
  • E. Terry O. Morse
    Terry O. Morse was an American film editor and director active in mid-20th-century Hollywood, known for his work on a variety of studio features.
  • 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: Stanley E. Johnson
Triple: [The Ladies Man, editedBy, Stanley E. Johnson]
Generated description
Stanley E. Johnson is a film editor known for his work on the movie "The Ladies Man."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Stanley E. Johnson
Target entity description: Stanley E. Johnson is a film editor known for his work on the movie "The Ladies Man."
  • A. Stanley E. Johnson
    Stanley E. Johnson is an editor best known for his work on the classic American children's novel "Old Yeller."
  • B. Stanley E. Johnson
    Stanley E. Johnson is a film editor known for his work on movies such as the comedy feature "The Bellboy."
  • C. Ronald E. Yates
    Ronald E. Yates is an American author and former journalist known for his work as a foreign correspondent and as a professor and dean at the University of Illinois’ College of Media.
  • D. Ronald L. Vaughn
    Ronald L. Vaughn is an American academic administrator best known for leading the University of Tampa through significant growth and development as its long-serving president.
  • E. Terry O. Morse
    Terry O. Morse was an American film editor and director active in mid-20th-century Hollywood, known for his work on a variety of studio features.
  • 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_69d6ab44a77c8190a652f4b27164e4ef completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d903b211688190bfe6dd15c3f96d2f completed April 10, 2026, 2:05 p.m.
NED1 Entity disambiguation (via context triple) batch_6a06f232ba1c8190a30e699c556625f6 completed May 15, 2026, 10:15 a.m.
NEDg Description generation batch_6a06f328c18881909948ed66d50b9eae completed May 15, 2026, 10:19 a.m.
NED2 Entity disambiguation (via description) batch_6a06f4f990808190b3b8e21f85d20219 completed May 15, 2026, 10:27 a.m.
Created at: April 8, 2026, 9:46 p.m.