Triple
T18440453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Numbuh 4 |
E450512
|
entity |
| Predicate | alias |
P39
|
FINISHED |
| Object |
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."
|
E1324229
|
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: [Numbuh 4, alias, Wally]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wally Context triple: [Numbuh 4, alias, 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 is a character from the comedy film "The Great Outdoors," known for his role in the movie’s humorous family vacation mishaps.
-
D.
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.
-
E.
Wally Fay
Wally Fay is a supporting character in the 1945 film noir "Mildred Pierce," known as a somewhat sleazy businessman entangled in the story’s web of betrayal and murder.
- 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: [Numbuh 4, alias, Wally]
Generated description
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."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wally Target entity description: 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."
-
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 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.
-
E.
Wally Fay
Wally Fay is a supporting character in the 1945 film noir "Mildred Pierce," known as a somewhat sleazy businessman entangled in the story’s web of betrayal and murder.
- 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_69d8d381d6388190a9e94e9c658174e4 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e51c10a86c819091196968b648fc92 |
completed | April 19, 2026, 6:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03f0b54224819084e67182674cfb83 |
completed | May 13, 2026, 3:32 a.m. |
| NEDg | Description generation | batch_6a03f2b7da408190b58c82fd5422a3c0 |
completed | May 13, 2026, 3:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03f32845348190963abbf6a039d2c6 |
completed | May 13, 2026, 3:42 a.m. |
Created at: April 10, 2026, 11:30 a.m.