Triple
T13667649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Death at a Funeral |
E327662
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Matthew Macfadyen |
E75520
|
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: Matthew Macfadyen | Statement: [Death at a Funeral, castMember, Matthew Macfadyen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matthew Macfadyen Context triple: [Death at a Funeral, castMember, Matthew Macfadyen]
-
A.
Matthew Macfadyen
chosen
Matthew Macfadyen is an English actor known for his versatile performances in film and television, including prominent roles in "Pride & Prejudice," "Succession," and various British dramas.
-
B.
Mattias Ferrell
Mattias Ferrell is one of the sons of American actor and comedian Will Ferrell.
-
C.
Seth Gabel
Seth Gabel is an American actor known for his roles in television series such as "Fringe," "Salem," and "Nip/Tuck."
-
D.
Zach Woods
Zach Woods is an American actor and comedian best known for his roles on television series such as "The Office," "Silicon Valley," and "Avenue 5."
-
E.
Max Greenfield
Max Greenfield is an American actor best known for his role as Schmidt on the television sitcom "New Girl."
- 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_69d8076f1fa8819094664a59b55010df |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc65832688190aea688fee0a7cbdb |
completed | April 12, 2026, 4:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f78b0cfe0c8190b0fe50931e9788cf |
completed | May 3, 2026, 5:51 p.m. |
Created at: April 9, 2026, 9:52 p.m.