Triple

T16097115
Position Surface form Disambiguated ID Type / Status
Subject The Joey Bishop Show E390515 entity
Predicate alsoStars P14987 FINISHED
Object Guy Marks
Guy Marks was an American comedian and character actor known for his appearances on 1960s television sitcoms and variety shows.
E1193999 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: Guy Marks | Statement: [The Joey Bishop Show, alsoStars, Guy Marks]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Guy Marks
Context triple: [The Joey Bishop Show, alsoStars, Guy Marks]
  • A. George Marks
    George Marks was a film editor known for his work on early American cinema, including the pioneering all-talking feature "Lights of New York."
  • B. Roy Marples
    Roy Marples is a software engineer best known for his work on the OpenRC init system and various networking tools in the Linux and BSD ecosystems.
  • C. Del James
    Del James is an American writer and musician best known for his work with Guns N’ Roses, including short stories that inspired some of the band’s iconic music videos.
  • D. Lee Boardman
    Lee Boardman is a British actor known for his roles in television dramas such as Rome and Coronation Street.
  • E. Billy Maharg
    Billy Maharg was an American gambler and former baseball player best known for his role as a go-between in the 1919 Black Sox World Series fixing scandal.
  • 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: Guy Marks
Triple: [The Joey Bishop Show, alsoStars, Guy Marks]
Generated description
Guy Marks was an American comedian and character actor known for his appearances on 1960s television sitcoms and variety shows.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Guy Marks
Target entity description: Guy Marks was an American comedian and character actor known for his appearances on 1960s television sitcoms and variety shows.
  • A. George Marks
    George Marks was a film editor known for his work on early American cinema, including the pioneering all-talking feature "Lights of New York."
  • B. Roy Marples
    Roy Marples is a software engineer best known for his work on the OpenRC init system and various networking tools in the Linux and BSD ecosystems.
  • C. Del James
    Del James is an American writer and musician best known for his work with Guns N’ Roses, including short stories that inspired some of the band’s iconic music videos.
  • D. Lee Boardman
    Lee Boardman is a British actor known for his roles in television dramas such as Rome and Coronation Street.
  • E. Billy Maharg
    Billy Maharg was an American gambler and former baseball player best known for his role as a go-between in the 1919 Black Sox World Series fixing scandal.
  • 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_69d87f198bc48190a8b7e53ca15b7ead completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e18593d0fc8190aa3ba3edb4219aaa completed April 17, 2026, 12:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffeb991ba88190ad568a49069f9701 completed May 10, 2026, 2:21 a.m.
NEDg Description generation batch_69ffec4898088190bed531e33418c7e5 completed May 10, 2026, 2:24 a.m.
NED2 Entity disambiguation (via description) batch_69ffecce96508190a53f100e3207ebac completed May 10, 2026, 2:26 a.m.
Created at: April 10, 2026, 4:59 a.m.