Triple

T13008641
Position Surface form Disambiguated ID Type / Status
Subject Snoopy, Come Home E322350 entity
Predicate editedBy P1954 FINISHED
Object Roger Donley
Roger Donley is a film editor known for his work on the animated feature "Snoopy, Come Home."
 E1028485 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: Roger Donley | Statement: [Snoopy, Come Home, editedBy, Roger Donley]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Roger Donley
Context triple: [Snoopy, Come Home, editedBy, Roger Donley]
  • A. Michael Kelso
    Michael Kelso is a dim-witted yet charming and good-looking teenager portrayed by Ashton Kutcher on the sitcom "That '70s Show."
  • B. Don Roderick
    Don Roderick is the legendary last Visigothic king of Spain, often depicted in literature as a tragic figure whose downfall heralds the Moorish conquest of the Iberian Peninsula.
  • C. Joe Noland
    Joe Noland is a fictional character from the television series "The District," which follows the professional and personal lives of law enforcement officials in Washington, D.C.
  • D. Phil Cunningham
    Phil Cunningham is an English guitarist and keyboardist best known for his work with the band New Order and previously with Marion.
  • E. Alan Osbiston
    Alan Osbiston was a British film editor known for his work on notable mid-20th-century films, including major war and drama productions.
  • 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: Roger Donley
Triple: [Snoopy, Come Home, editedBy, Roger Donley]
Generated description
Roger Donley is a film editor known for his work on the animated feature "Snoopy, Come Home."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Roger Donley
Target entity description: Roger Donley is a film editor known for his work on the animated feature "Snoopy, Come Home."
  • A. Michael Kelso
    Michael Kelso is a dim-witted yet charming and good-looking teenager portrayed by Ashton Kutcher on the sitcom "That '70s Show."
  • B. Don Roderick
    Don Roderick is the legendary last Visigothic king of Spain, often depicted in literature as a tragic figure whose downfall heralds the Moorish conquest of the Iberian Peninsula.
  • C. Joe Noland
    Joe Noland is a fictional character from the television series "The District," which follows the professional and personal lives of law enforcement officials in Washington, D.C.
  • D. Phil Cunningham
    Phil Cunningham is an English guitarist and keyboardist best known for his work with the band New Order and previously with Marion.
  • E. Alan Osbiston
    Alan Osbiston was a British film editor known for his work on notable mid-20th-century films, including major war and drama productions.
  • 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_69d807657e8c8190bd9435ee2f823845 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97e9cf0108190b02f498c6ccc91f8 completed April 10, 2026, 10:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6ff00a2f48190b88babba80521818 completed May 3, 2026, 7:53 a.m.
NEDg Description generation batch_69f703be3d8c8190aa004eb15dfdc98e completed May 3, 2026, 8:13 a.m.
NED2 Entity disambiguation (via description) batch_69f70441c874819097e91125667a41f5 completed May 3, 2026, 8:16 a.m.
Created at: April 9, 2026, 8:48 p.m.