Triple

T23101184
Position Surface form Disambiguated ID Type / Status
Subject Death in Brunswick E576033 entity
Predicate mainCharacter P1183 FINISHED
Object Dave
Dave is the hapless, darkly comic protagonist of the Australian black comedy film "Death in Brunswick."
E1571538 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: Dave | Statement: [Death in Brunswick, mainCharacter, Dave]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dave
Context triple: [Death in Brunswick, mainCharacter, Dave]
  • A. Dave
    Dave is a common masculine given name, often a shortened form of David, used widely in English-speaking countries.
  • B. Dave
    "Dave" is a 1993 political comedy film about a presidential look-alike who unexpectedly finds himself acting as the President of the United States.
  • C. Dave
    Dave is a character from Dr. Seuss’s children’s book collection "The Sneetches and Other Stories," featured in one of its moral-driven tales.
  • D. Danny
    Danny is a masculine given name, often used as a diminutive of Daniel.
  • E. Danny
    Danny is the central protagonist of the film "Lowriders," a young man torn between his passion for street art and the expectations of his lowrider-obsessed family in East Los Angeles.
  • 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: Dave
Triple: [Death in Brunswick, mainCharacter, Dave]
Generated description
Dave is the hapless, darkly comic protagonist of the Australian black comedy film "Death in Brunswick."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Dave
Target entity description: Dave is the hapless, darkly comic protagonist of the Australian black comedy film "Death in Brunswick."
  • A. Dave
    Dave is a common masculine given name, often a shortened form of David, used widely in English-speaking countries.
  • B. Dave
    "Dave" is a 1993 political comedy film about a presidential look-alike who unexpectedly finds himself acting as the President of the United States.
  • C. Dave
    Dave is a character from Dr. Seuss’s children’s book collection "The Sneetches and Other Stories," featured in one of its moral-driven tales.
  • D. Danny
    Danny is the central protagonist of the film "Lowriders," a young man torn between his passion for street art and the expectations of his lowrider-obsessed family in East Los Angeles.
  • E. Danny
    Danny is the central character in the short story "In the Gloaming," around whom the narrative’s emotional and thematic developments revolve.
  • 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_69e245c060b48190a9bd61a47a16db17 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f18de8c5b4819095cddf989cade60d completed April 29, 2026, 4:49 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0c23e26fcc81908c83314244c8bc44 completed May 19, 2026, 8:48 a.m.
NEDg Description generation batch_6a0c27f16a188190ac5eeb3c0dbe2ac4 completed May 19, 2026, 9:05 a.m.
NED2 Entity disambiguation (via description) batch_6a0c28f3c9288190b9ee0e5c4911ee9b completed May 19, 2026, 9:10 a.m.
Created at: April 17, 2026, 3:58 p.m.