Triple

T20488180
Position Surface form Disambiguated ID Type / Status
Subject Bella Spewack E502661 entity
Predicate givenName P17 FINISHED
Object Bella
Bella is a feminine given name commonly used in various cultures, often as a shortened form of Isabella or Arabella.
 E1119972 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: [Bella Spewack, givenName, Bella]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bella
Context triple: [Bella Spewack, givenName, Bella]
  • A. Bella
    Bella is a 2006 independent drama film starring Tammy Blanchard that explores themes of love, redemption, and unexpected family.
  • B. Bella
    Bella is a character from Quentin Blake’s children’s picture book "Zagazoo," which humorously explores the chaos and transformations of childhood.
  • C. Bella
    Bella is one of the two iconic Liver Bird statues that sit atop the Royal Liver Building in Liverpool, symbolizing the city and its maritime heritage.
  • D. Bella
    Bella is the given name of Australian actress Bella Heathcote, known for her roles in film and television.
  • 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: [Bella Spewack, givenName, Bella]
Generated description
Bella is a feminine given name commonly used in various cultures, often as a shortened form of Isabella or Arabella.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bella
Target entity description: Bella is a feminine given name commonly used in various cultures, often as a shortened form of Isabella or Arabella.
  • A. Bella chosen
    Bella is a feminine given name commonly used in various cultures, often as a diminutive of names like Isabella or Arabella.
  • B. Bella
    Bella is the given name of Australian actress Bella Heathcote, known for her roles in film and television.
  • C. Bella
    Bella is the main human protagonist of the Twilight series, known for her introspective nature and complex relationship with the supernatural world.
  • D. Bella
    Bella is a character from Quentin Blake’s children’s picture book "Zagazoo," which humorously explores the chaos and transformations of childhood.
  • E. Bella
    Bella is a 2006 independent drama film starring Tammy Blanchard that explores themes of love, redemption, and unexpected family.
  • F. None of above.

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_69e0b4b0373881909dd3e9387f82eab4 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e69b5c6f84819087d813be3542ed33 completed April 20, 2026, 9:32 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0893b3c77c81908aedd753ec251577 completed May 16, 2026, 3:56 p.m.
NEDg Description generation batch_6a08953618088190a9336d0ab6445c9d completed May 16, 2026, 4:03 p.m.
NED2 Entity disambiguation (via description) batch_6a0895c4a0a48190ba43d065c9766fd8 completed May 16, 2026, 4:05 p.m.
Created at: April 16, 2026, 11:34 a.m.