Triple

T16910110
Position Surface form Disambiguated ID Type / Status
Subject Death to Smoochy E410170 entity
Predicate character P662 FINISHED
Object Burke Bennett
Burke Bennett is a character in the dark comedy film "Death to Smoochy," involved in the corrupt and cutthroat world of children's television.
 E1239753 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: Burke Bennett | Statement: [Death to Smoochy, character, Burke Bennett]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Burke Bennett
Context triple: [Death to Smoochy, character, Burke Bennett]
  • A. Lisa Bennett
    Lisa Bennett is the paternal grandmother of Marli Bennett.
  • B. Darcy Elliott
    Darcy Elliott is the protagonist of the work "For Keeps?", around whom the central narrative and character development revolve.
  • C. Parker Bennett
    Parker Bennett is an American screenwriter best known for co-writing the 1993 live-action film adaptation of Super Mario Bros.
  • D. Taylor Bennett
    Taylor Bennett is an American rapper and songwriter from Chicago known for his independent releases and for being the younger brother of Chance the Rapper.
  • E. Laura Barton
    Laura Barton is a supporting character in the Marvel Cinematic Universe, known as Clint Barton's wife and a grounding, family-oriented presence in the Hawkeye storyline.
  • 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: Burke Bennett
Triple: [Death to Smoochy, character, Burke Bennett]
Generated description
Burke Bennett is a character in the dark comedy film "Death to Smoochy," involved in the corrupt and cutthroat world of children's television.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Burke Bennett
Target entity description: Burke Bennett is a character in the dark comedy film "Death to Smoochy," involved in the corrupt and cutthroat world of children's television.
  • A. Lisa Bennett
    Lisa Bennett is the paternal grandmother of Marli Bennett.
  • B. Darcy Elliott
    Darcy Elliott is the protagonist of the work "For Keeps?", around whom the central narrative and character development revolve.
  • C. Parker Bennett
    Parker Bennett is an American screenwriter best known for co-writing the 1993 live-action film adaptation of Super Mario Bros.
  • D. Taylor Bennett
    Taylor Bennett is an American rapper and songwriter from Chicago known for his independent releases and for being the younger brother of Chance the Rapper.
  • E. Laura Barton
    Laura Barton is a supporting character in the Marvel Cinematic Universe, known as Clint Barton's wife and a grounding, family-oriented presence in the Hawkeye storyline.
  • 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_69d886c7b1e481908c3766dfa8c13458 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3ca3ca0c481909ff361ccf4a922e3 completed April 18, 2026, 6:15 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00c7bb4ac481909318d3d61a2d10e1 completed May 10, 2026, 6 p.m.
NEDg Description generation batch_6a00c8c9c78481908e503977d47f7c1f completed May 10, 2026, 6:04 p.m.
NED2 Entity disambiguation (via description) batch_6a00c9d1c6a0819083635b8246cc82e7 completed May 10, 2026, 6:09 p.m.
Created at: April 10, 2026, 5:30 a.m.