Triple
T7558021
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Devil and Daniel Webster |
E178721
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Dan Totheroh |
E689589
|
NE FINISHED |
How this triple was built (2 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: [The Devil and Daniel Webster, screenwriter, Dan Totheroh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Totheroh Context triple: [The Devil and Daniel Webster, screenwriter, Dan Totheroh]
-
A.
Dan Totheroh
chosen
Dan Totheroh was an American playwright, screenwriter, and occasional actor known for his work in early 20th-century theater and film.
-
B.
Eric Danchick
Eric Danchick is a film producer known for his work on the movie "Bound 2."
-
C.
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."
-
D.
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.
-
E.
Dan Rydell
Dan Rydell is a charismatic, quick-witted sports anchor and one of the central protagonists on the television series "Sports Night."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c69f2da22c8190a50942ac20af70e8 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f8db3d508190850ed41854d69838 |
completed | March 27, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c9c89d62c08190b575d7e1058afbeb |
completed | March 30, 2026, 12:49 a.m. |
Created at: March 27, 2026, 3:50 p.m.