Triple
T7806710
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter Vaughan |
E180573
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Peter Vaughan |
E180573
|
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: Peter Vaughan | Statement: [Peter Vaughan, name, Peter Vaughan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter Vaughan Context triple: [Peter Vaughan, name, Peter Vaughan]
-
A.
Peter Vaughan
chosen
Peter Vaughan was a British character actor known for his powerful screen presence in film and television, including roles in works like "Brazil," "Straw Dogs," and later "Game of Thrones."
-
B.
Stephen Pycroft
Stephen Pycroft is a British businessman best known as the founder of the construction and consultancy company Mace Group.
-
C.
Michael Gwynn
Michael Gwynn was a British character actor known for his roles in mid-20th-century film and television, including appearances in classic horror and science fiction productions.
-
D.
Gavin Thorpe
Gavin Thorpe is a British author and game designer best known for his novels and work on the Warhammer and Warhammer 40,000 universes for Games Workshop and Black Library.
-
E.
John Ashton
John Ashton is an American character actor best known for his tough, often blue-collar roles in crime and action films such as "Beverly Hills Cop" and "Midnight Run."
- 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_69ca827f6f148190beca4e245b993506 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69caf639fba88190a7c117aab19c0b0f |
completed | March 30, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb1450a2408190afe0459086d4480f |
completed | March 31, 2026, 12:24 a.m. |
Created at: March 30, 2026, 4:35 p.m.