Triple

T15368322
Position Surface form Disambiguated ID Type / Status
Subject Catch .44 E367472 entity
Predicate mainCharacter P1183 FINISHED
Object Dawn
Dawn is a central character in the crime thriller film "Catch .44," around whom much of the movie’s tense, intersecting plot revolves.
E1152828 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: Dawn | Statement: [Catch .44, mainCharacter, Dawn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dawn
Context triple: [Catch .44, mainCharacter, Dawn]
  • A. Dawn
    Dawn is a novel by Elie Wiesel that explores the moral and psychological struggles of a young Holocaust survivor involved in a Jewish underground movement in British-controlled Palestine.
  • B. Dawn
    Dawn was a NASA space probe that studied the protoplanet Vesta and the dwarf planet Ceres in the asteroid belt using ion propulsion.
  • C. Dawn
    "Dawn" is a lesser-known novel by American author Eleanor H. Porter, best known for writing "Pollyanna."
  • D. Dawn
    Dawn is a leading American dishwashing liquid brand known for its strong grease-cutting power and use in wildlife rescue efforts.
  • E. Dawn
    Dawn is a feminine given name commonly associated with the early morning time when light first appears in the sky.
  • 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: Dawn
Triple: [Catch .44, mainCharacter, Dawn]
Generated description
Dawn is a central character in the crime thriller film "Catch .44," around whom much of the movie’s tense, intersecting plot revolves.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Dawn
Target entity description: Dawn is a central character in the crime thriller film "Catch .44," around whom much of the movie’s tense, intersecting plot revolves.
  • A. Dawn
    Dawn is a novel by Elie Wiesel that explores the moral and psychological struggles of a young Holocaust survivor involved in a Jewish underground movement in British-controlled Palestine.
  • B. Dawn
    Dawn is a feminine given name commonly associated with the early morning time when light first appears in the sky.
  • C. Dawn
    Dawn is a leading American dishwashing liquid brand known for its strong grease-cutting power and use in wildlife rescue efforts.
  • D. Dawn
    Dawn is a science fiction novel by Octavia E. Butler that opens her Xenogenesis (Lilith’s Brood) trilogy, exploring themes of alien contact, genetic manipulation, and the future of humanity.
  • E. Dawn
    "Dawn" is a lesser-known novel by American author Eleanor H. Porter, best known for writing "Pollyanna."
  • 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_69d85a1483788190ad93c2748e8af34b completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e4a7cdc8190b7b48c97e774c306 completed April 16, 2026, 1:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff0b50703881909ca71c985bc1c7b5 completed May 9, 2026, 10:24 a.m.
NEDg Description generation batch_69ff0cb1b9188190b0eb99661d26206f completed May 9, 2026, 10:30 a.m.
NED2 Entity disambiguation (via description) batch_69ff0d3182f08190abd463d921e1830e completed May 9, 2026, 10:32 a.m.
Created at: April 10, 2026, 3:18 a.m.