Triple

T20403142
Position Surface form Disambiguated ID Type / Status
Subject Killer Force E500388 entity
Predicate screenwriter P2831 FINISHED
Object Michael Winder
Michael Winder is a screenwriter best known for his work on the 1976 action film "Killer Force."
E1465690 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: Michael Winder | Statement: [Killer Force, screenwriter, Michael Winder]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Winder
Context triple: [Killer Force, screenwriter, Michael Winder]
  • A. Michael Wimer
    Michael Wimer is a film and television producer best known for his work on genre projects such as the science fiction series "Outsiders."
  • B. Michael Wittenberg
    Michael Wittenberg was an investment adviser best known as the late husband of Broadway star Bernadette Peters.
  • C. Michael Weller
    Michael Weller is an American playwright and screenwriter best known for his work on stage and film in the 1970s and 1980s, including the screenplay for the movie adaptation of the musical "Hair."
  • D. Phil Wandscher
    Phil Wandscher is an American guitarist best known as a founding member and lead guitarist of the alt-country band Whiskeytown.
  • E. Mel Winkler
    Mel Winkler was an American character actor best known for his distinctive voice work in video games and animation, as well as supporting roles in film and television.
  • 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: Michael Winder
Triple: [Killer Force, screenwriter, Michael Winder]
Generated description
Michael Winder is a screenwriter best known for his work on the 1976 action film "Killer Force."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Michael Winder
Target entity description: Michael Winder is a screenwriter best known for his work on the 1976 action film "Killer Force."
  • A. Michael Wimer
    Michael Wimer is a film and television producer best known for his work on genre projects such as the science fiction series "Outsiders."
  • B. Michael Wittenberg
    Michael Wittenberg was an investment adviser best known as the late husband of Broadway star Bernadette Peters.
  • C. Michael Weller
    Michael Weller is an American playwright and screenwriter best known for his work on stage and film in the 1970s and 1980s, including the screenplay for the movie adaptation of the musical "Hair."
  • D. Phil Wandscher
    Phil Wandscher is an American guitarist best known as a founding member and lead guitarist of the alt-country band Whiskeytown.
  • E. Mel Winkler
    Mel Winkler was an American character actor best known for his distinctive voice work in video games and animation, as well as supporting roles in film and television.
  • 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_69e0b4a81bec8190b69adfdc1336a015 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6799080cc819096dc31f41d1d7b49 completed April 20, 2026, 7:08 p.m.
NED1 Entity disambiguation (via context triple) batch_6a095a3e3f448190b06f8d189fb7dfc5 completed May 17, 2026, 6:03 a.m.
NEDg Description generation batch_6a095ab1e4e08190ac86e53ccee269f3 completed May 17, 2026, 6:05 a.m.
NED2 Entity disambiguation (via description) batch_6a095b71e4c08190a41566557503b9a8 completed May 17, 2026, 6:08 a.m.
Created at: April 16, 2026, 11:29 a.m.