Triple
T1191823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leslie Nielsen |
E25377
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Mr. Magoo
Mr. Magoo is a near-sighted, bumbling cartoon character known for getting into comical misadventures due to his poor vision.
|
E136826
|
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: Mr. Magoo | Statement: [Leslie Nielsen, notableWork, Mr. Magoo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mr. Magoo Context triple: [Leslie Nielsen, notableWork, Mr. Magoo]
-
A.
Yosemite Sam
Yosemite Sam is a hot-tempered, mustachioed outlaw and recurring antagonist in the Looney Tunes cartoons, known for his fiery personality and frequent clashes with Bugs Bunny.
-
B.
Moe
Moe is the nickname of Moe Berg, an American baseball player who famously served as a spy during World War II.
-
C.
Pepé Le Pew
Pepé Le Pew is a romantic, overly confident skunk from the Looney Tunes cartoons, best known for his comedic, French-accented pursuit of love.
-
D.
Garfield
Garfield is best known as the 20th president of the United States, whose term in 1881 was cut short by assassination.
-
E.
Foghorn Leghorn
Foghorn Leghorn is a loud, fast-talking, Southern-accented cartoon rooster from the Looney Tunes series known for his comedic antics and catchphrases.
- 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: Mr. Magoo Triple: [Leslie Nielsen, notableWork, Mr. Magoo]
Generated description
Mr. Magoo is a near-sighted, bumbling cartoon character known for getting into comical misadventures due to his poor vision.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mr. Magoo Target entity description: Mr. Magoo is a near-sighted, bumbling cartoon character known for getting into comical misadventures due to his poor vision.
-
A.
Yosemite Sam
Yosemite Sam is a hot-tempered, mustachioed outlaw and recurring antagonist in the Looney Tunes cartoons, known for his fiery personality and frequent clashes with Bugs Bunny.
-
B.
Moe
Moe is the nickname of Moe Berg, an American baseball player who famously served as a spy during World War II.
-
C.
Pepé Le Pew
Pepé Le Pew is a romantic, overly confident skunk from the Looney Tunes cartoons, best known for his comedic, French-accented pursuit of love.
-
D.
Garfield
Garfield is best known as the 20th president of the United States, whose term in 1881 was cut short by assassination.
-
E.
Foghorn Leghorn
Foghorn Leghorn is a loud, fast-talking, Southern-accented cartoon rooster from the Looney Tunes series known for his comedic antics and catchphrases.
- 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_69a49427d98881908646d6c63b8cea1e |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd74e2c08190b4a48425f94addaa |
completed | March 1, 2026, 10:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac764ea588819082f7d5d0e44e1211 |
completed | March 7, 2026, 7:02 p.m. |
| NEDg | Description generation | batch_69ac76e1b430819092669c6e83d7a62c |
completed | March 7, 2026, 7:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac7768daa4819082a07fa755ce7364 |
completed | March 7, 2026, 7:07 p.m. |
Created at: March 1, 2026, 7:45 p.m.