Triple

T12374462
Position Surface form Disambiguated ID Type / Status
Subject Happy Endings E295085 entity
Predicate mainCharacter P1183 FINISHED
Object Max Blum
Max Blum is a sarcastic, laid-back, and often underachieving gay man who provides much of the offbeat humor in the ensemble sitcom "Happy Endings."
 E984798 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: Max Blum | Statement: [Happy Endings, mainCharacter, Max Blum]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Max Blum
Context triple: [Happy Endings, mainCharacter, Max Blum]
  • A. Michael Blum
    Michael Blum is best known as the husband of comedian and actress Julia Sweeney.
  • B. Len Blum
    Len Blum is a Canadian screenwriter known for his work on numerous comedy films, including the 2006 reboot of The Pink Panther.
  • C. Michael Schiffer
    Michael Schiffer is an American screenwriter and playwright best known for scripting films such as "Lean on Me," "Crimson Tide," and "The Peacemaker."
  • D. Christopher Franke
    Christopher Franke is a German composer and former Tangerine Dream member best known for his electronic and film scores, including work on science fiction and adventure productions.
  • E. Michael Bergmann
    Michael Bergmann is an American analytic philosopher known for his work in epistemology, particularly on skepticism, justification, and religious epistemology.
  • 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: Max Blum
Triple: [Happy Endings, mainCharacter, Max Blum]
Generated description
Max Blum is a sarcastic, laid-back, and often underachieving gay man who provides much of the offbeat humor in the ensemble sitcom "Happy Endings."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Max Blum
Target entity description: Max Blum is a sarcastic, laid-back, and often underachieving gay man who provides much of the offbeat humor in the ensemble sitcom "Happy Endings."
  • A. Michael Blum
    Michael Blum is best known as the husband of comedian and actress Julia Sweeney.
  • B. Len Blum
    Len Blum is a Canadian screenwriter known for his work on numerous comedy films, including the 2006 reboot of The Pink Panther.
  • C. Michael Schiffer
    Michael Schiffer is an American screenwriter and playwright best known for scripting films such as "Lean on Me," "Crimson Tide," and "The Peacemaker."
  • D. Christopher Franke
    Christopher Franke is a German composer and former Tangerine Dream member best known for his electronic and film scores, including work on science fiction and adventure productions.
  • E. Michael Bergmann
    Michael Bergmann is an American analytic philosopher known for his work in epistemology, particularly on skepticism, justification, and religious epistemology.
  • 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_69d6ab6d8a4081908636601e69ddf262 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93fa8ca7c8190b3f8e9c2ec23e837 completed April 10, 2026, 6:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69f63ef6084c8190960f0df7e10066e2 completed May 2, 2026, 6:14 p.m.
NEDg Description generation batch_69f640874a0481908d9203b48304d866 completed May 2, 2026, 6:20 p.m.
NED2 Entity disambiguation (via description) batch_69f641287f888190bc7000c256c362d3 completed May 2, 2026, 6:23 p.m.
Created at: April 8, 2026, 9:54 p.m.