Triple
T5101092
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ted Lasso |
E114981
|
entity |
| Predicate | creator |
P184
|
FINISHED |
| Object |
Joe Kelly
Joe Kelly is a television writer and producer best known as one of the co-creators of the acclaimed comedy series "Ted Lasso."
|
E494425
|
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: Joe Kelly | Statement: [Ted Lasso, creator, Joe Kelly]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joe Kelly Context triple: [Ted Lasso, creator, Joe Kelly]
-
A.
Kevin O'Connell
Kevin O'Connell is an American football coach and former NFL quarterback who serves as the head coach of the Minnesota Vikings.
-
B.
David Bell
David Bell is the protagonist of the 1987 American drama film "Americana," around whom the story’s emotional and narrative arc revolves.
-
C.
Jason Keller
Jason Keller is an American screenwriter best known for co-writing the racing drama film "Ford v Ferrari."
-
D.
Matt O'Leary
Matt O'Leary is an American actor best known for his roles in early 2000s films such as the Spy Kids franchise and various independent and genre movies.
-
E.
Andrew Gallant
Andrew Gallant is a software engineer best known for creating and maintaining ripgrep, a fast command-line search tool.
- 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: Joe Kelly Triple: [Ted Lasso, creator, Joe Kelly]
Generated description
Joe Kelly is a television writer and producer best known as one of the co-creators of the acclaimed comedy series "Ted Lasso."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Joe Kelly Target entity description: Joe Kelly is a television writer and producer best known as one of the co-creators of the acclaimed comedy series "Ted Lasso."
-
A.
Kevin O'Connell
Kevin O'Connell is an American football coach and former NFL quarterback who serves as the head coach of the Minnesota Vikings.
-
B.
David Bell
David Bell is the protagonist of the 1987 American drama film "Americana," around whom the story’s emotional and narrative arc revolves.
-
C.
Jason Keller
Jason Keller is an American screenwriter best known for co-writing the racing drama film "Ford v Ferrari."
-
D.
Matt O'Leary
Matt O'Leary is an American actor best known for his roles in early 2000s films such as the Spy Kids franchise and various independent and genre movies.
-
E.
Andrew Gallant
Andrew Gallant is a software engineer best known for creating and maintaining ripgrep, a fast command-line search tool.
- 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_69bd4440b3348190be1251fd8b7951f1 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7584ed408190a6d1086588f24faa |
completed | March 20, 2026, 4:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beba8d24388190882b9933a2a798c4 |
completed | March 21, 2026, 3:34 p.m. |
| NEDg | Description generation | batch_69bebbe7e8e081909814e97001f8cf89 |
completed | March 21, 2026, 3:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bebd33f25c8190a5d9b78ef71847e3 |
completed | March 21, 2026, 3:45 p.m. |
Created at: March 20, 2026, 1:40 p.m.