Triple

T13508754
Position Surface form Disambiguated ID Type / Status
Subject How to Be Both E321081 entity
Predicate mainCharacter P1183 FINISHED
Object George
George is a teenage girl in Ali Smith’s novel "How to Be Both," whose grief, curiosity, and obsession with art drive one of the book’s intertwined narrative strands.
 E1044714 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: George | Statement: [How to Be Both, mainCharacter, George]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: George
Context triple: [How to Be Both, mainCharacter, George]
  • A. George
    George is the given first name of the fictional character Gob Bluth from the television series "Arrested Development."
  • B. George
    George is the middle name of William George Barker, a renowned Canadian World War I flying ace and Victoria Cross recipient.
  • C. George
    George is the given name of George Stanley, 9th Baron Strange, an English nobleman and politician of the late 15th century.
  • D. George
    George is the given name of George Carnegie, 6th Earl of Northesk, a Scottish nobleman and naval officer in the Royal Navy.
  • E. George
    George is the given name of Lord George Murray, a prominent Scottish Jacobite general during the 18th-century uprisings.
  • 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: George
Triple: [How to Be Both, mainCharacter, George]
Generated description
George is a teenage girl in Ali Smith’s novel "How to Be Both," whose grief, curiosity, and obsession with art drive one of the book’s intertwined narrative strands.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: George
Target entity description: George is a teenage girl in Ali Smith’s novel "How to Be Both," whose grief, curiosity, and obsession with art drive one of the book’s intertwined narrative strands.
  • A. George
    George is the young, curious protagonist of Lucy and Stephen Hawking’s children’s science-adventure book series, where he explores the universe and big scientific ideas.
  • B. George
    George is the curious young protagonist of the children's science adventure book series "George's Secret Key to the Universe," co-authored by Stephen Hawking.
  • C. George
    George is one of the central child protagonists in Enid Blyton’s Famous Five series, known for her tomboyish nature, courage, and love of adventure.
  • D. George
    George is one of the adventurous child protagonists in Enid Blyton’s Famous Five series, known for her tomboyish nature, courage, and strong-willed independence.
  • E. George
    George is one of the main child protagonists in Enid Blyton’s Famous Five series, known for her tomboyish nature, courage, and love of adventure.
  • 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_69d807629d6c8190998f1b9bb12d2ed0 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbaf85a74081909eb08751fc55ce8f completed April 12, 2026, 2:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7547f6b1c8190965b239da0b47e93 completed May 3, 2026, 1:58 p.m.
NEDg Description generation batch_69f756088214819091b1e9e2de9f5f68 completed May 3, 2026, 2:04 p.m.
NED2 Entity disambiguation (via description) batch_69f7565cab308190b4afdbfbc7f6264b completed May 3, 2026, 2:06 p.m.
Created at: April 9, 2026, 9:43 p.m.