Triple

T8448287
Position Surface form Disambiguated ID Type / Status
Subject John "Breacher" Wharton E199735 entity
Predicate fullName P16 FINISHED
Object John Wharton
John Wharton is a fictional character known by the nickname "Breacher," typically portrayed as a tough, combat-hardened military or special-operations figure.
 E775002 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: John Wharton | Statement: [John "Breacher" Wharton, fullName, John Wharton]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: John Wharton
Context triple: [John "Breacher" Wharton, fullName, John Wharton]
  • A. William Wharton
    William Wharton is a sadistic, unhinged death row inmate and key antagonist in Stephen King’s novel "The Green Mile."
  • B. Joseph Wightman
    Joseph Wightman was a British Army officer best known for commanding government forces against the Jacobites during the early 18th-century uprisings.
  • C. Alan Whiting
    Alan Whiting is an astronomer known for his discovery of the Cetus Dwarf Galaxy.
  • D. Ian Ward
    Ian Ward is a personal name shared by several notable individuals, including professionals in fields such as sports, academia, and the arts.
  • E. William Walsh
    William Walsh is a name shared by several notable individuals, including politicians, writers, and athletes, whose specific identity depends on the context in which it is used.
  • 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: John Wharton
Triple: [John "Breacher" Wharton, fullName, John Wharton]
Generated description
John Wharton is a fictional character known by the nickname "Breacher," typically portrayed as a tough, combat-hardened military or special-operations figure.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: John Wharton
Target entity description: John Wharton is a fictional character known by the nickname "Breacher," typically portrayed as a tough, combat-hardened military or special-operations figure.
  • A. William Wharton
    William Wharton is a sadistic, unhinged death row inmate and key antagonist in Stephen King’s novel "The Green Mile."
  • B. Joseph Wightman
    Joseph Wightman was a British Army officer best known for commanding government forces against the Jacobites during the early 18th-century uprisings.
  • C. Alan Whiting
    Alan Whiting is an astronomer known for his discovery of the Cetus Dwarf Galaxy.
  • D. Ian Ward
    Ian Ward is a personal name shared by several notable individuals, including professionals in fields such as sports, academia, and the arts.
  • E. William Walsh
    William Walsh is a name shared by several notable individuals, including politicians, writers, and athletes, whose specific identity depends on the context in which it is used.
  • 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_69ca83170f9081909cd98f55614c6476 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe445b7988190b53ae45070c70d1d completed March 31, 2026, 3:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69cfeace262881909dedeb1a07e95279 completed April 3, 2026, 4:29 p.m.
NEDg Description generation batch_69cfec2bde708190b73c7672625e912d completed April 3, 2026, 4:34 p.m.
NED2 Entity disambiguation (via description) batch_69cfee809b54819089d4d0d5c555e428 completed April 3, 2026, 4:44 p.m.
Created at: March 30, 2026, 6:09 p.m.