Triple
T7720203
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malvern College |
E174987
|
entity |
| Predicate | hasAlumnus |
P51
|
FINISHED |
| Object | Jeremy Paxman |
E88999
|
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 Paxman | Statement: [Malvern College, hasAlumnus, Jeremy Paxman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeremy Paxman Context triple: [Malvern College, hasAlumnus, Jeremy Paxman]
-
A.
Jeremy Paxman
chosen
Jeremy Paxman is a British broadcaster, journalist, and author best known for his incisive interviewing style on BBC’s Newsnight and as the long-time host of the quiz show University Challenge.
-
B.
Andrew Marr
Andrew Marr is a prominent British journalist, broadcaster, and political commentator best known for presenting BBC current affairs programmes such as "The Andrew Marr Show."
-
C.
Giles Paxman
Giles Paxman is a British former diplomat who served as the United Kingdom's ambassador to Mexico and later to Spain.
-
D.
Matthew Parris
Matthew Parris is a British political commentator, columnist, and former Conservative MP known for his incisive and often witty analysis of UK politics.
-
E.
Lem Dobbs
Lem Dobbs is a British-American screenwriter known for his work on films such as "Dark City," "The Limey," and "The Score."
- 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_69c6995d541c81909eaa646b1a8369a9 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702eedc088190be645c029dfc462a |
completed | March 27, 2026, 10:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8b517c64881908d24e8613dc33bf4 |
completed | March 29, 2026, 5:13 a.m. |
Created at: March 27, 2026, 4:05 p.m.