Triple

T16238297
Position Surface form Disambiguated ID Type / Status
Subject Zagazoo E394172 entity
Predicate hasCharacter P2308 FINISHED
Object Bella
Bella is a character from Quentin Blake’s children’s picture book "Zagazoo," which humorously explores the chaos and transformations of childhood.
 E1202827 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: Bella | Statement: [Zagazoo, hasCharacter, Bella]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bella
Context triple: [Zagazoo, hasCharacter, Bella]
  • A. Bella
    Bella is the main human protagonist of the Twilight series, known for her introspective nature and complex relationship with the supernatural world.
  • B. Bella
    Bella is a 2006 independent drama film starring Tammy Blanchard that explores themes of love, redemption, and unexpected family.
  • C. Bella
    Bella is the given name of Australian actress Bella Heathcote, known for her roles in film and television.
  • D. Bella
    Bella is a feminine given name commonly used in various cultures, often as a diminutive of names like Isabella or Arabella.
  • E. Bella
    Bella is a close friend of William Thacker, the fictional London bookseller portrayed by Hugh Grant in the romantic comedy film "Notting Hill."
  • 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: Bella
Triple: [Zagazoo, hasCharacter, Bella]
Generated description
Bella is a character from Quentin Blake’s children’s picture book "Zagazoo," which humorously explores the chaos and transformations of childhood.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bella
Target entity description: Bella is a character from Quentin Blake’s children’s picture book "Zagazoo," which humorously explores the chaos and transformations of childhood.
  • A. Bella
    Bella is the main human protagonist of the Twilight series, known for her introspective nature and complex relationship with the supernatural world.
  • B. Bella
    Bella is a 2006 independent drama film starring Tammy Blanchard that explores themes of love, redemption, and unexpected family.
  • C. Bella
    Bella is the given name of Australian actress Bella Heathcote, known for her roles in film and television.
  • D. Bella
    Bella is a close friend of William Thacker, the fictional London bookseller portrayed by Hugh Grant in the romantic comedy film "Notting Hill."
  • E. Bella
    Bella is a feminine given name commonly used in various cultures, often as a diminutive of names like Isabella or Arabella.
  • 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_69d87f2171208190951025e526947816 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e2455c7a3c81909e3b42edf03be43e completed April 17, 2026, 2:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a000edaf76c8190acc01f58845e570a completed May 10, 2026, 4:51 a.m.
NEDg Description generation batch_6a0011f7f3fc8190a3a3bf260b391e78 completed May 10, 2026, 5:04 a.m.
NED2 Entity disambiguation (via description) batch_6a0012e9913481908ee5ada2fce507f4 completed May 10, 2026, 5:08 a.m.
Created at: April 10, 2026, 5:04 a.m.