Triple
T22307733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blundell’s School |
E551427
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Peter Blundell |
—
|
NE NERFINISHED |
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 Blundell | Statement: [Blundell’s School, namedAfter, Peter Blundell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter Blundell Context triple: [Blundell’s School, namedAfter, Peter Blundell]
-
A.
Peter Blundell
chosen
Peter Blundell was an English merchant and philanthropist best known for endowing educational institutions in the late 16th century.
-
B.
Charles Blundell
Charles Blundell is a machine learning researcher known for his contributions to deep learning and probabilistic modeling, including work on few-shot learning methods.
-
C.
Jason Blundell
Jason Blundell is a video game developer best known as a key creative lead on the Call of Duty: Black Ops Zombies mode at Treyarch.
-
D.
Peter Yeldham
Peter Yeldham was an Australian screenwriter and playwright known for his prolific work in film, television, and radio from the mid-20th century onward.
-
E.
Peter Blaker
Peter Blaker was a British Conservative politician who served in several ministerial roles, particularly in defense and foreign affairs, during the 1970s and early 1980s.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e46c0188190800181a4233f28fe |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1574bccb08190a6236dd14cf0fc5b |
completed | April 29, 2026, 12:56 a.m. |
Created at: April 16, 2026, 8:41 p.m.