Triple
T12783132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Muse |
E305555
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Mark Feuerstein |
E1012703
|
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: Mark Feuerstein | Statement: [The Muse, castMember, Mark Feuerstein]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Feuerstein Context triple: [The Muse, castMember, Mark Feuerstein]
-
A.
Mark Feuerstein
chosen
Mark Feuerstein is an American actor best known for his lead role as Dr. Hank Lawson on the television series "Royal Pains."
-
B.
Steven Fierberg
Steven Fierberg is an American cinematographer known for his work on feature films and television series, including the romantic drama "Love & Other Drugs."
-
C.
Stephen Endlicher
Stephen Endlicher was a 19th-century Austrian botanist and linguist known for his influential work in plant taxonomy and classification.
-
D.
Robert Weinbach
Robert Weinbach is a film producer known for his work on independent genre movies, including the 2012 horror film "Shiver."
-
E.
Daniel Goldberg
Daniel Goldberg is a Canadian film producer best known for his long-running collaboration with Ivan Reitman on comedies such as "Meatballs," "Stripes," and "Old School."
- 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_69d7bdf2b43c819098ae5aa68e61ea58 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e5b52048190b279b7ad066efe9f |
completed | April 10, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6cbb735e481909f120f95fa68f4f1 |
completed | May 3, 2026, 4:14 a.m. |
Created at: April 9, 2026, 5:29 p.m.