Triple

T11343087
Position Surface form Disambiguated ID Type / Status
Subject Lord Auckland E268648 entity
Predicate givenName P17 FINISHED
Object George
George is the given name of Lord Auckland, a British statesman and colonial administrator of the 19th century.
 E919609 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: [Lord Auckland, givenName, George]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: George
Context triple: [Lord Auckland, givenName, George]
  • A. George
    George is the heroic protagonist of the fantasy film "The Magic Sword," known for embarking on a perilous quest to rescue a princess from an evil sorcerer.
  • B. George
    George is a common English surname of likely Greek and Latin origin, associated with numerous notable historical and contemporary figures.
  • C. George
    George is the given name of George Murray, 6th Duke of Atholl, a Scottish peer and nobleman of the 19th century.
  • D. George
    George is the given name of George de Hevesy, the Hungarian radiochemist and Nobel laureate known for pioneering the use of radioactive tracers in studying chemical processes.
  • E. George
    George is a supporting character in the romantic comedy film "27 Dresses," serving as a colleague and love interest within the story’s central wedding-planning world.
  • 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: [Lord Auckland, givenName, George]
Generated description
George is the given name of Lord Auckland, a British statesman and colonial administrator of the 19th century.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: George
Target entity description: George is the given name of Lord Auckland, a British statesman and colonial administrator of the 19th century.
  • A. George
    George is the given name of Sir George Grey, a prominent 19th-century British colonial governor and statesman.
  • 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 Montagu-Dunk, 2nd Earl of Halifax, an influential 18th-century British statesman and colonial administrator.
  • D. George
    George is the given name of George Spencer-Churchill, 6th Duke of Marlborough, a British aristocrat and politician of the 19th century.
  • E. George
    George is the given name of Sir George Collier, a British Royal Navy officer known for his service during the late 18th and early 19th 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_69d6aacb1f0881908c84a349fd1be047 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7ea1e360c8190a02d1e2d1d6f4b5d completed April 9, 2026, 6:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69e542df01fc81908539407e20543002 completed April 19, 2026, 9:02 p.m.
NEDg Description generation batch_69e545ad9840819096cb11f1d427ea38 completed April 19, 2026, 9:14 p.m.
NED2 Entity disambiguation (via description) batch_69e548c50aac81909f94ac2f35a29f41 completed April 19, 2026, 9:27 p.m.
Created at: April 8, 2026, 9:33 p.m.