Triple

T19907496
Position Surface form Disambiguated ID Type / Status
Subject Dilbert E478456 entity
Predicate mainCharacter P1183 FINISHED
Object Wally
Wally is a perpetually lazy, coffee-loving office worker from the "Dilbert" comic strip, known for his extreme apathy and talent for avoiding work.
 E1400847 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: Wally | Statement: [Dilbert, mainCharacter, Wally]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wally
Context triple: [Dilbert, mainCharacter, Wally]
  • A. Wally
    Wally is a character featured in the educational children's series "Alphabetical Order," likely serving as a playful figure to help teach letters and literacy concepts.
  • B. Wally
    Wally is a common English diminutive given name, typically derived from names like Walter or Waldemar.
  • C. Wally
    Wally, better known as Numbuh 4, is a hot-headed, tough but loyal member of the Kids Next Door from the animated series "Codename: Kids Next Door."
  • D. Wally
    Wally is a character from the comedy film "The Great Outdoors," known for his role in the movie’s humorous family vacation mishaps.
  • E. Wally Mars
    Wally Mars is a central character in the romantic comedy film "The Switch," known as the neurotic best friend whose actions inadvertently lead to an unconventional parenthood twist.
  • 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: Wally
Triple: [Dilbert, mainCharacter, Wally]
Generated description
Wally is a perpetually lazy, coffee-loving office worker from the "Dilbert" comic strip, known for his extreme apathy and talent for avoiding work.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Wally
Target entity description: Wally is a perpetually lazy, coffee-loving office worker from the "Dilbert" comic strip, known for his extreme apathy and talent for avoiding work.
  • A. Wally
    Wally is a character featured in the educational children's series "Alphabetical Order," likely serving as a playful figure to help teach letters and literacy concepts.
  • B. Wally
    Wally is a common English diminutive given name, typically derived from names like Walter or Waldemar.
  • C. Wally
    Wally is a character from the comedy film "The Great Outdoors," known for his role in the movie’s humorous family vacation mishaps.
  • D. Wally
    Wally, better known as Numbuh 4, is a hot-headed, tough but loyal member of the Kids Next Door from the animated series "Codename: Kids Next Door."
  • E. Wally Mars
    Wally Mars is a central character in the romantic comedy film "The Switch," known as the neurotic best friend whose actions inadvertently lead to an unconventional parenthood twist.
  • 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_69d8e520682081909892916424699bd5 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e6598cc5108190bca2a47c9f8ef70f completed April 20, 2026, 4:51 p.m.
NED1 Entity disambiguation (via context triple) batch_6a07ea494af08190ac8c43797bff5012 completed May 16, 2026, 3:53 a.m.
NEDg Description generation batch_6a07ee6d8ef481908987eef826425806 completed May 16, 2026, 4:11 a.m.
NED2 Entity disambiguation (via description) batch_6a07eec5b4788190b697a0b7382b5c2b completed May 16, 2026, 4:12 a.m.
Created at: April 10, 2026, 1:52 p.m.