Triple

T10705710
Position Surface form Disambiguated ID Type / Status
Subject Prince George of Wales E252397 entity
Predicate givenName P17 FINISHED
Object George
George is the eldest child of Prince William and Catherine, Princess of Wales, and second in line to the British throne.
E880361 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: [Prince George of Wales, givenName, George]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: George
Context triple: [Prince George of Wales, givenName, 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 given name of George Stanley, 9th Baron Strange, an English nobleman and politician of the late 15th century.
  • C. George
    George is a middle-aged, embittered history professor whose caustic wit and psychological games drive the intense marital drama in Edward Albee’s play "Who’s Afraid of Virginia Woolf?".
  • D. George
    George is the given name of George Washington Gale Ferris Jr., the American engineer best known for inventing the original Ferris wheel.
  • E. George
    George is the given name of George Carnegie, 6th Earl of Northesk, a Scottish nobleman and naval officer in the Royal Navy.
  • 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: [Prince George of Wales, givenName, George]
Generated description
George is the eldest child of Prince William and Catherine, Princess of Wales, and second in line to the British throne.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: George
Target entity description: George is the eldest child of Prince William and Catherine, Princess of Wales, and second in line to the British throne.
  • A. George
    George is the given name of George Reginald Oliver Molyneux Herbert, the 8th Earl of Carnarvon, a British aristocrat associated with Highclere Castle.
  • B. George
    George is the given name of George Spencer, 4th Duke of Marlborough, an 18th-century British nobleman and politician.
  • C. George
    George is the given name of George Spencer-Churchill, 6th Duke of Marlborough, a British aristocrat and politician of the 19th century.
  • D. George
    George is the given name of Lord George Cavendish, a British aristocrat and politician from the prominent Cavendish family.
  • E. George
    George is the given name of George Montagu-Dunk, 2nd Earl of Halifax, an influential 18th-century British statesman and colonial administrator.
  • 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_69d6aa5cbabc8190973e683950d89faf completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6fddeb060819094cd125a68070eb2 completed April 9, 2026, 1:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69d998fe56dc8190ae0c987b28ec6206 completed April 11, 2026, 12:42 a.m.
NEDg Description generation batch_69d99e8632688190b3746649a124ca09 completed April 11, 2026, 1:06 a.m.
NED2 Entity disambiguation (via description) batch_69da625a1e8c8190b282e7a70bb7c876 completed April 11, 2026, 3:01 p.m.
Created at: April 8, 2026, 9:12 p.m.