Triple

T10368163
Position Surface form Disambiguated ID Type / Status
Subject Bad Santa E244308 entity
Predicate mainCharacter P1183 FINISHED
Object Thurman Merman
Thurman Merman is the naive, lonely young boy in the dark comedy film "Bad Santa" who forms an unlikely bond with the cynical, alcoholic mall Santa.
 E861462 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: Thurman Merman | Statement: [Bad Santa, mainCharacter, Thurman Merman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Thurman Merman
Context triple: [Bad Santa, mainCharacter, Thurman Merman]
  • A. Jack Basehart
    Jack Basehart is the son of American actor Richard Basehart, known for his work in film and television during the mid-20th century.
  • B. Lewis Allen
    Lewis Allen was a British-born film and television director best known for his atmospheric work in mid-20th-century Hollywood cinema.
  • C. Lewis Allen
    Lewis Allen was a local figure of significance after whom the city of Allen Park, Michigan, was named.
  • D. Otto Hunte
    Otto Hunte was a prominent German film art director and production designer best known for his influential work on classic Weimar-era films, including Fritz Lang’s Metropolis.
  • E. Roy Harlow
    Roy Harlow was the husband of silent film actress Marie Mosquini, known primarily in relation to her career in early American cinema.
  • 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: Thurman Merman
Triple: [Bad Santa, mainCharacter, Thurman Merman]
Generated description
Thurman Merman is the naive, lonely young boy in the dark comedy film "Bad Santa" who forms an unlikely bond with the cynical, alcoholic mall Santa.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Thurman Merman
Target entity description: Thurman Merman is the naive, lonely young boy in the dark comedy film "Bad Santa" who forms an unlikely bond with the cynical, alcoholic mall Santa.
  • A. Jack Basehart
    Jack Basehart is the son of American actor Richard Basehart, known for his work in film and television during the mid-20th century.
  • B. Lewis Allen
    Lewis Allen was a British-born film and television director best known for his atmospheric work in mid-20th-century Hollywood cinema.
  • C. Lewis Allen
    Lewis Allen was a local figure of significance after whom the city of Allen Park, Michigan, was named.
  • D. Otto Hunte
    Otto Hunte was a prominent German film art director and production designer best known for his influential work on classic Weimar-era films, including Fritz Lang’s Metropolis.
  • E. Roy Harlow
    Roy Harlow was the husband of silent film actress Marie Mosquini, known primarily in relation to her career in early American cinema.
  • 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_69d381b3e328819094b23b8edcd29b5a completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e97106448190a075948e63184f47 completed April 7, 2026, 11:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69d7fb8e96e081908282bb0f82719abe completed April 9, 2026, 7:18 p.m.
NEDg Description generation batch_69d822d303888190aa556287b3b1cc03 completed April 9, 2026, 10:06 p.m.
NED2 Entity disambiguation (via description) batch_69d859b05a3881908c97cb173d160e44 completed April 10, 2026, 2 a.m.
Created at: April 6, 2026, 12:01 p.m.