Triple

T15735636
Position Surface form Disambiguated ID Type / Status
Subject Gugu Mbatha-Raw E381462 entity
Predicate notableWork P4 FINISHED
Object Belle
Belle is a 2013 British period drama film inspired by the true story of Dido Elizabeth Belle, a mixed-race woman raised in an aristocratic English family, exploring themes of race, class, and social justice.
 E1173494 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: Belle | Statement: [Gugu Mbatha-Raw, notableWork, Belle]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Belle
Context triple: [Gugu Mbatha-Raw, notableWork, Belle]
  • A. Belle
    Belle is a supporting character in the 2018 heist thriller film "Widows," involved in the criminal plot led by a group of women in Chicago.
  • B. Belle
    Belle is a British television drama film featuring Thomas Geoffrey Wilkinson in a prominent role.
  • C. Belle
    "Belle" is a mellow, acoustic-driven song by Jack Johnson featured on his 2005 album *In Between Dreams*.
  • D. Belle
    Belle is the intelligent, book-loving heroine of Disney’s "Beauty and the Beast," known for her compassion, independence, and iconic yellow ball gown.
  • E. Belle
    Belle is the given name of Belle W. Baruch, an American philanthropist, conservationist, and heiress to the Baruch family fortune.
  • 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: Belle
Triple: [Gugu Mbatha-Raw, notableWork, Belle]
Generated description
Belle is a 2013 British period drama film inspired by the true story of Dido Elizabeth Belle, a mixed-race woman raised in an aristocratic English family, exploring themes of race, class, and social justice.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Belle
Target entity description: Belle is a 2013 British period drama film inspired by the true story of Dido Elizabeth Belle, a mixed-race woman raised in an aristocratic English family, exploring themes of race, class, and social justice.
  • A. Belle
    Belle is a British television drama film featuring Thomas Geoffrey Wilkinson in a prominent role.
  • B. Belle
    Belle is the intelligent, book-loving heroine of Disney’s "Beauty and the Beast," known for her compassion, independence, and iconic yellow ball gown.
  • C. Belle
    Belle is a supporting character in the 2018 heist thriller film "Widows," involved in the criminal plot led by a group of women in Chicago.
  • D. Belle
    Belle is the given name of Belle W. Baruch, an American philanthropist, conservationist, and heiress to the Baruch family fortune.
  • E. Belle
    Belle Roosevelt was an American socialite and member of the prominent Roosevelt family in the late 19th and early 20th centuries.
  • 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_69d86d9cdb648190bf3171be0bd7d872 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e04fd586a88190aa1b1b88368d386f completed April 16, 2026, 2:56 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff8300a4248190ba52573b57f31b36 completed May 9, 2026, 6:54 p.m.
NEDg Description generation batch_69ff8378450081909614f68772a23851 completed May 9, 2026, 6:56 p.m.
NED2 Entity disambiguation (via description) batch_69ff84125e808190a4d465d9effad639 completed May 9, 2026, 6:59 p.m.
Created at: April 10, 2026, 4:46 a.m.