Triple
T12476715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Who Is America? |
E298192
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object | Dan Mazer |
E320685
|
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 Mazer | Statement: [Who Is America?, executiveProducer, Dan Mazer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Mazer Context triple: [Who Is America?, executiveProducer, Dan Mazer]
-
A.
Dan Mazer
chosen
Dan Mazer is a British screenwriter, director, and producer best known for his long-time collaboration with Sacha Baron Cohen on projects like Borat and Brüno.
-
B.
Dan Mazeau
Dan Mazeau is an American screenwriter known for working on major Hollywood action and fantasy films, including entries in the Fast & Furious franchise.
-
C.
David Javerbaum
David Javerbaum is an American comedy writer and producer best known for his work as a head writer and executive producer on The Daily Show with Jon Stewart and for co-authoring several of its related books.
-
D.
Michael Merrill
Michael Merrill is the son of legendary American actress Bette Davis, known for his later role in managing her estate and legacy.
-
E.
Dan Grossman
Dan Grossman is a computer scientist and professor known for his work in programming languages and software engineering.
- 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_69d6ada377208190a36011199a4d8558 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94dcc24e48190ae9c367a03f659f4 |
completed | April 10, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6cbb096d881908dfd2a7126632d96 |
completed | May 3, 2026, 4:14 a.m. |
Created at: April 8, 2026, 9:56 p.m.