Triple
T15206570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Candy |
E363403
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
John
John is the given name of the late Canadian comedian and actor John Candy, known for his roles in films like "Planes, Trains and Automobiles" and "Uncle Buck."
|
E363403
|
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 | Statement: [John Candy, givenName, John]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Context triple: [John Candy, givenName, John]
-
A.
John
John is the given first name of Johnny Kilbane, an American featherweight boxing champion from the early 20th century.
-
B.
John
John is the given name of John Albert William Spencer-Churchill, a British aristocrat and 10th Duke of Marlborough.
-
C.
John
John is the given first name of the 19th-century English theologian and social reformer Frederick Denison Maurice.
-
D.
John
John is the given name of John Eales, the renowned former Australian rugby union captain and World Cup winner.
-
E.
John
John is the given name of Sir John Lennard-Jones, a pioneering British theoretical chemist known for his work on intermolecular forces and the Lennard-Jones potential.
- 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 Triple: [John Candy, givenName, John]
Generated description
John is the given name of the late Canadian comedian and actor John Candy, known for his roles in films like "Planes, Trains and Automobiles" and "Uncle Buck."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: John Target entity description: John is the given name of the late Canadian comedian and actor John Candy, known for his roles in films like "Planes, Trains and Automobiles" and "Uncle Buck."
-
A.
John
chosen
John is the given name of the late Canadian actor and comedian John Candy, known for his roles in films like "Planes, Trains and Automobiles" and "Uncle Buck."
-
B.
John
John is the given name of the late American comedian and actor John Belushi, famed for his work on "Saturday Night Live" and in films like "Animal House" and "The Blues Brothers."
-
C.
John
John is the given name of American actor John Goodman, renowned for his roles in film, television, and theater.
-
D.
John
John is the given name of John Witherspoon, an American actor and comedian best known for his roles in the "Friday" film series and the television show "The Wayans Bros."
-
E.
John
John is the given name of English comedian, actor, and writer John Cleese, a founding member of the legendary comedy group Monty Python.
- F. None of above.
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_69d85a0b78bc8190b6e5ad51a2c4cfc5 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e006b7964c8190bc8dc3444b94f15e |
completed | April 15, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fed3304464819083732a23e650c649 |
completed | May 9, 2026, 6:24 a.m. |
| NEDg | Description generation | batch_69fed46caba481908864d62936659a6d |
completed | May 9, 2026, 6:30 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fed4f5be0c8190a1fb6e1a176c5208 |
completed | May 9, 2026, 6:32 a.m. |
Created at: April 10, 2026, 3:11 a.m.