Triple
T1125724
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Super Bowl II |
E24714
|
entity |
| Predicate | announcer |
P7529
|
FINISHED |
| Object |
Ray Scott
Ray Scott was a renowned American sportscaster best known for his minimalist, understated play-by-play style on NFL broadcasts.
|
E135415
|
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: Ray Scott | Statement: [Super Bowl II, announcer, Ray Scott]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ray Scott Context triple: [Super Bowl II, announcer, Ray Scott]
-
A.
Craig Bierko
Craig Bierko is an American actor known for his work in film, television, and theater, often playing charismatic or villainous roles.
-
B.
David Gamble
David Gamble is a film editor best known for his work on the Academy Award–winning romantic comedy-drama "Shakespeare in Love."
-
C.
Tony Gayton
Tony Gayton is an American screenwriter and producer best known for co-creating the Western television drama series "Hell on Wheels."
-
D.
Tom Bell
Tom Bell was an American football official best known for serving as the referee in Super Bowl III.
-
E.
Joel McNeely
Joel McNeely is an American composer and conductor best known for his work on film and television scores, including numerous projects for Disney and other major studios.
- 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: Ray Scott Triple: [Super Bowl II, announcer, Ray Scott]
Generated description
Ray Scott was a renowned American sportscaster best known for his minimalist, understated play-by-play style on NFL broadcasts.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ray Scott Target entity description: Ray Scott was a renowned American sportscaster best known for his minimalist, understated play-by-play style on NFL broadcasts.
-
A.
Craig Bierko
Craig Bierko is an American actor known for his work in film, television, and theater, often playing charismatic or villainous roles.
-
B.
David Gamble
David Gamble is a film editor best known for his work on the Academy Award–winning romantic comedy-drama "Shakespeare in Love."
-
C.
Tony Gayton
Tony Gayton is an American screenwriter and producer best known for co-creating the Western television drama series "Hell on Wheels."
-
D.
Tom Bell
Tom Bell was an American football official best known for serving as the referee in Super Bowl III.
-
E.
Joel McNeely
Joel McNeely is an American composer and conductor best known for his work on film and television scores, including numerous projects for Disney and other major studios.
- 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_69a4940712c88190aa244f3fc6070a65 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bbdaf2d4819086f480f69da127f9 |
completed | March 1, 2026, 10:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac6f11a31481909e11a01b12841b3d |
completed | March 7, 2026, 6:31 p.m. |
| NEDg | Description generation | batch_69ac71788d0081909d7931319d93db36 |
completed | March 7, 2026, 6:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac71cd1f048190b8b4aa48a878d8b7 |
completed | March 7, 2026, 6:43 p.m. |
Created at: March 1, 2026, 7:44 p.m.