Triple

T21676032
Position Surface form Disambiguated ID Type / Status
Subject Mannish Boy E534972 entity
Predicate composer P1361 FINISHED
Object Mel London
Mel London was an American blues songwriter and producer known for penning influential Chicago blues standards in the 1950s and 1960s.
 E1496313 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: Mel London | Statement: [Mannish Boy, composer, Mel London]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mel London
Context triple: [Mannish Boy, composer, Mel London]
  • A. John Hurst
    John Hurst is a personal name shared by multiple notable individuals across fields such as sports, politics, and the arts.
  • B. John Lanchbery
    John Lanchbery was a renowned British conductor and arranger best known for his influential work in ballet music for companies such as The Royal Ballet.
  • C. Jack Garland
    Jack Garland was a prominent Canadian politician from North Bay, Ontario, who served as a long-time Member of Parliament and community leader.
  • D. Roy Lunn
    Roy Lunn is a character portrayed by actor JJ Feild, likely within a film or television production.
  • E. George Marks
    George Marks was a film editor known for his work on early American cinema, including the pioneering all-talking feature "Lights of New York."
  • 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: Mel London
Triple: [Mannish Boy, composer, Mel London]
Generated description
Mel London was an American blues songwriter and producer known for penning influential Chicago blues standards in the 1950s and 1960s.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mel London
Target entity description: Mel London was an American blues songwriter and producer known for penning influential Chicago blues standards in the 1950s and 1960s.
  • A. John Hurst
    John Hurst is a personal name shared by multiple notable individuals across fields such as sports, politics, and the arts.
  • B. John Lanchbery
    John Lanchbery was a renowned British conductor and arranger best known for his influential work in ballet music for companies such as The Royal Ballet.
  • C. Jack Garland
    Jack Garland was a prominent Canadian politician from North Bay, Ontario, who served as a long-time Member of Parliament and community leader.
  • D. Roy Lunn
    Roy Lunn is a character portrayed by actor JJ Feild, likely within a film or television production.
  • E. George Marks
    George Marks was a film editor known for his work on early American cinema, including the pioneering all-talking feature "Lights of New York."
  • 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_69e0c46898008190aa618a4af55bd1ee completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69ef8a0f85748190944b9425ffed02ee completed April 27, 2026, 4:08 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0a1e1715b88190aee6109d2f6e5667 completed May 17, 2026, 7:59 p.m.
NEDg Description generation batch_6a0a1ebd2bbc819083021cbc5873bfed completed May 17, 2026, 8:02 p.m.
NED2 Entity disambiguation (via description) batch_6a0a1fa12dbc8190a263b12f75ef073b completed May 17, 2026, 8:05 p.m.
Created at: April 16, 2026, 6:42 p.m.