Triple
T7241650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rollie Totheroh |
E155369
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object |
Dan Totheroh
Dan Totheroh was an American playwright, screenwriter, and occasional actor known for his work in early 20th-century theater and film.
|
E689589
|
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: Dan Totheroh | Statement: [Rollie Totheroh, relative, Dan Totheroh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Totheroh Context triple: [Rollie Totheroh, relative, Dan Totheroh]
-
A.
Eric Danchick
Eric Danchick is a film producer known for his work on the movie "Bound 2."
-
B.
Dan Haggerty
Dan Haggerty was an American actor best known for his portrayal of the gentle mountain man in the film and television series "The Life and Times of Grizzly Adams."
-
C.
Dan Pfeiffer
Dan Pfeiffer is an American political strategist and former White House communications director who served as a senior adviser to President Barack Obama.
-
D.
Dan Rydell
Dan Rydell is a charismatic, quick-witted sports anchor and one of the central protagonists on the television series "Sports Night."
-
E.
Frank Doelger
Frank Doelger is a television producer best known for his work on the acclaimed HBO fantasy series "Game of Thrones."
- 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: Dan Totheroh Triple: [Rollie Totheroh, relative, Dan Totheroh]
Generated description
Dan Totheroh was an American playwright, screenwriter, and occasional actor known for his work in early 20th-century theater and film.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dan Totheroh Target entity description: Dan Totheroh was an American playwright, screenwriter, and occasional actor known for his work in early 20th-century theater and film.
-
A.
Eric Danchick
Eric Danchick is a film producer known for his work on the movie "Bound 2."
-
B.
Dan Haggerty
Dan Haggerty was an American actor best known for his portrayal of the gentle mountain man in the film and television series "The Life and Times of Grizzly Adams."
-
C.
Dan Pfeiffer
Dan Pfeiffer is an American political strategist and former White House communications director who served as a senior adviser to President Barack Obama.
-
D.
Dan Rydell
Dan Rydell is a charismatic, quick-witted sports anchor and one of the central protagonists on the television series "Sports Night."
-
E.
Frank Doelger
Frank Doelger is a television producer best known for his work on the acclaimed HBO fantasy series "Game of Thrones."
- 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_69c688143bfc81908d4176617735e601 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ea552a688190a00f5d0ad982f787 |
completed | March 27, 2026, 8:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c902aadd308190bb130386af68464e |
completed | March 29, 2026, 10:44 a.m. |
| NEDg | Description generation | batch_69c90347dd348190a08615a8c9d07899 |
completed | March 29, 2026, 10:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c903c0a1c08190a7d32998bee36d6e |
completed | March 29, 2026, 10:49 a.m. |
Created at: March 27, 2026, 2:55 p.m.