Triple
T5177871
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Super Bowl LVI |
E116843
|
entity |
| Predicate | referee |
P268
|
FINISHED |
| Object |
Ron Torbert
Ron Torbert is an American NFL official who has served as a referee in multiple high-profile games, including Super Bowl LVI.
|
E500033
|
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: Ron Torbert | Statement: [Super Bowl LVI, referee, Ron Torbert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ron Torbert Context triple: [Super Bowl LVI, referee, Ron Torbert]
-
A.
Dan Rydell
Dan Rydell is a charismatic, quick-witted sports anchor and one of the central protagonists on the television series "Sports Night."
-
B.
Mick Rogers
Mick Rogers is an Australian former professional road cyclist known for his time-trialling strength and multiple world championship titles in the team time trial.
-
C.
Lou Jacobi
Lou Jacobi was a Canadian-born character actor known for his comedic roles in film, television, and theater, particularly in mid-20th-century Hollywood and Broadway productions.
-
D.
Tom Elkins
Tom Elkins is a film editor best known for his work in the horror and thriller genres, including editing movies like "Inferno."
-
E.
Jeff Fager
Jeff Fager is an American television producer best known for leading and shaping the long-running CBS news magazine program "60 Minutes."
- 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: Ron Torbert Triple: [Super Bowl LVI, referee, Ron Torbert]
Generated description
Ron Torbert is an American NFL official who has served as a referee in multiple high-profile games, including Super Bowl LVI.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ron Torbert Target entity description: Ron Torbert is an American NFL official who has served as a referee in multiple high-profile games, including Super Bowl LVI.
-
A.
Dan Rydell
Dan Rydell is a charismatic, quick-witted sports anchor and one of the central protagonists on the television series "Sports Night."
-
B.
Mick Rogers
Mick Rogers is an Australian former professional road cyclist known for his time-trialling strength and multiple world championship titles in the team time trial.
-
C.
Lou Jacobi
Lou Jacobi was a Canadian-born character actor known for his comedic roles in film, television, and theater, particularly in mid-20th-century Hollywood and Broadway productions.
-
D.
Tom Elkins
Tom Elkins is a film editor best known for his work in the horror and thriller genres, including editing movies like "Inferno."
-
E.
Jeff Fager
Jeff Fager is an American television producer best known for leading and shaping the long-running CBS news magazine program "60 Minutes."
- 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_69bd446140f08190becb93c61158f27f |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7976339481909ece900de22064f2 |
completed | March 20, 2026, 4:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bed95185ac819085fb42a69e014ec5 |
completed | March 21, 2026, 5:45 p.m. |
| NEDg | Description generation | batch_69bedb0e6d248190b099c2b282efde19 |
completed | March 21, 2026, 5:53 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bedb7c82d081908141c775cbed881e |
completed | March 21, 2026, 5:55 p.m. |
Created at: March 20, 2026, 1:45 p.m.