Triple
T13038673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeremy Northam |
E326631
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Jeremy Northam |
E326631
|
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: Jeremy Northam | Statement: [Jeremy Northam, name, Jeremy Northam]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeremy Northam Context triple: [Jeremy Northam, name, Jeremy Northam]
-
A.
Jeremy Northam
chosen
Jeremy Northam is an English actor known for his versatile film and television roles, including period dramas and character-driven ensemble pieces.
-
B.
John Aston
John Aston is an actor known for his role in the film "The Navigators."
-
C.
James Bassett
James Bassett was an American novelist best known for writing the World War II naval novel "Harm's Way," which was adapted into the 1965 film "In Harm's Way."
-
D.
John Radcliffe
John Radcliffe was an influential 17th-century English physician and royal doctor whose wealth funded several major buildings at the University of Oxford.
-
E.
Michael York
Michael York is an English actor known for his roles in films such as "Cabaret," "Logan's Run," and the "Austin Powers" series.
- 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d9804d8e3081909584c93df099859a |
completed | April 10, 2026, 10:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6e269c18481908e0b46c298a946ca |
completed | May 3, 2026, 5:51 a.m. |
Created at: April 9, 2026, 8:55 p.m.