Triple

T4761325
Position Surface form Disambiguated ID Type / Status
Subject Krampus (2015 film) E105703 entity
Predicate mainCharacter P1183 FINISHED
Object Tom Engel
Tom Engel is the young boy protagonist in the 2015 horror-comedy film "Krampus," whose loss of Christmas spirit triggers the arrival of the folkloric monster.
E466921 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: Tom Engel | Statement: [Krampus (2015 film), mainCharacter, Tom Engel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tom Engel
Context triple: [Krampus (2015 film), mainCharacter, Tom Engel]
  • A. John Eisendrath
    John Eisendrath is a television writer and producer best known for his work on series such as "The Blacklist" and "Alias."
  • B. Hal Bidlack
    Hal Bidlack is an American political science professor, retired U.S. Air Force officer, and public speaker known for his work in skepticism and secular humanism.
  • C. Marc Ewing
    Marc Ewing is an American software engineer and entrepreneur best known as a co-founder of the open-source software company Red Hat.
  • D. Tom Leppert
    Tom Leppert is an American businessman and politician who served as the mayor of Dallas, Texas, and later ran for the U.S. Senate.
  • E. Bob Tzudiker
    Bob Tzudiker is an American screenwriter best known for co-writing Disney animated films such as "Tarzan" and "The Lion King."
  • 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: Tom Engel
Triple: [Krampus (2015 film), mainCharacter, Tom Engel]
Generated description
Tom Engel is the young boy protagonist in the 2015 horror-comedy film "Krampus," whose loss of Christmas spirit triggers the arrival of the folkloric monster.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Tom Engel
Target entity description: Tom Engel is the young boy protagonist in the 2015 horror-comedy film "Krampus," whose loss of Christmas spirit triggers the arrival of the folkloric monster.
  • A. John Eisendrath
    John Eisendrath is a television writer and producer best known for his work on series such as "The Blacklist" and "Alias."
  • B. Hal Bidlack
    Hal Bidlack is an American political science professor, retired U.S. Air Force officer, and public speaker known for his work in skepticism and secular humanism.
  • C. Marc Ewing
    Marc Ewing is an American software engineer and entrepreneur best known as a co-founder of the open-source software company Red Hat.
  • D. Tom Leppert
    Tom Leppert is an American businessman and politician who served as the mayor of Dallas, Texas, and later ran for the U.S. Senate.
  • E. Bob Tzudiker
    Bob Tzudiker is an American screenwriter best known for co-writing Disney animated films such as "Tarzan" and "The Lion King."
  • 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_69bd43f14cac819081c7c69803648211 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd650eefe08190b99f9f01b121dbfd completed March 20, 2026, 3:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69be3a7c72c48190a562d6261e323b4b completed March 21, 2026, 6:28 a.m.
NEDg Description generation batch_69be3c81c7b4819099d2627e7aa1611c completed March 21, 2026, 6:36 a.m.
NED2 Entity disambiguation (via description) batch_69be3d0aa1d48190a2af91d251cb5561 completed March 21, 2026, 6:39 a.m.
Created at: March 20, 2026, 1:20 p.m.