Triple

T11300906
Position Surface form Disambiguated ID Type / Status
Subject Mickey Leland E267582 entity
Predicate given name P17 FINISHED
Object George
George is the given first name of American politician and civil rights leader Mickey Leland.
 E919595 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: [Mickey Leland, given name, George]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: George
Context triple: [Mickey Leland, given name, 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: [Mickey Leland, given name, George]
Generated description
George is the given first name of American politician and civil rights leader Mickey Leland.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: George
Target entity description: George is the given first name of American politician and civil rights leader Mickey Leland.
  • A. George
    George is the first name of George Washington, the first President of the United States and a key leader in the American Revolutionary War.
  • B. George
    George is the given name of George W. Norris, a prominent early 20th-century American politician known for his progressive reforms and long service in the U.S. Congress.
  • C. George
    George is the given name of George Gordon Battle Liddy, the American lawyer and political operative best known for his role in the Watergate scandal.
  • D. George
    George is the given first name of G. Gordon Liddy, the former FBI agent and key operative in the Watergate scandal.
  • E. George
    George is the given name of George W. McLaurin, the first African American student admitted to the University of Oklahoma.
  • 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_69d6aac993a08190a6f36445ebaf9a43 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e9a4aad4819097384e1b591be2e3 completed April 9, 2026, 6:02 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, 9:32 p.m.