Triple
T15328105
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Rocks |
E366463
|
entity |
| Predicate | headCoach |
P256
|
FINISHED |
| Object |
Tom Farden
Tom Farden is an American gymnastics coach best known for leading the University of Utah’s Red Rocks women’s gymnastics program.
|
E1161396
|
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: Tom Farden | Statement: [Red Rocks, headCoach, Tom Farden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Farden Context triple: [Red Rocks, headCoach, Tom Farden]
-
A.
Scott Fagan
Scott Fagan is an American singer-songwriter associated with the late-1960s psychedelic and folk-rock scenes, whose work later gained cult recognition.
-
B.
Tom Lofaro
Tom Lofaro is a television producer best known for his executive production work on the long-running comedy series "It's Always Sunny in Philadelphia."
-
C.
John Farris
John Farris is an American novelist and screenwriter best known for his horror and suspense fiction, including the novel that inspired Brian De Palma’s film "The Fury."
-
D.
Dan Fagan
Dan Fagan is a notable individual recognized for achievements associated with the surname Fagan.
-
E.
Michael Fetterly
Michael Fetterly is a voice actor known for portraying the character Uncle Fester in an adaptation of The Addams Family.
- 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: Tom Farden Triple: [Red Rocks, headCoach, Tom Farden]
Generated description
Tom Farden is an American gymnastics coach best known for leading the University of Utah’s Red Rocks women’s gymnastics program.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tom Farden Target entity description: Tom Farden is an American gymnastics coach best known for leading the University of Utah’s Red Rocks women’s gymnastics program.
-
A.
Scott Fagan
Scott Fagan is an American singer-songwriter associated with the late-1960s psychedelic and folk-rock scenes, whose work later gained cult recognition.
-
B.
Tom Lofaro
Tom Lofaro is a television producer best known for his executive production work on the long-running comedy series "It's Always Sunny in Philadelphia."
-
C.
John Farris
John Farris is an American novelist and screenwriter best known for his horror and suspense fiction, including the novel that inspired Brian De Palma’s film "The Fury."
-
D.
Dan Fagan
Dan Fagan is a notable individual recognized for achievements associated with the surname Fagan.
-
E.
Michael Fetterly
Michael Fetterly is a voice actor known for portraying the character Uncle Fester in an adaptation of The Addams Family.
- 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_69d85a121520819093dcce999fdefe1a |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03dffd6f88190a0f031ee90c6a7d2 |
completed | April 16, 2026, 1:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3d3cf84c8190a4655803b12c9721 |
completed | May 9, 2026, 1:57 p.m. |
| NEDg | Description generation | batch_69ff3e05ba088190a49f8a765397923d |
completed | May 9, 2026, 2 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff3e69d7fc8190b8c5f99d3c8158f6 |
completed | May 9, 2026, 2:02 p.m. |
Created at: April 10, 2026, 3:16 a.m.