Triple
T19470708
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Young Mungo |
E487114
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | Douglas Stuart |
—
|
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: Douglas Stuart | Statement: [Young Mungo, author, Douglas Stuart]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Douglas Stuart Context triple: [Young Mungo, author, Douglas Stuart]
-
A.
Douglas Stuart
chosen
Douglas Stuart is a Scottish-American novelist best known for his debut novel "Shuggie Bain," which won the Booker Prize.
-
B.
Marlon James
Marlon James is a Jamaican novelist and Booker Prize winner known for his ambitious, genre-blending works that explore history, violence, and Black identity.
-
C.
Eleanor Catton
Eleanor Catton is a New Zealand novelist best known for her Booker Prize–winning historical novel "The Luminaries."
-
D.
Dermot Povey
Dermot Povey is a fictional character from the British sitcom "Men Behaving Badly," known as one of the show's central male leads.
-
E.
Martin Sixsmith
Martin Sixsmith is a British journalist, author, and former BBC correspondent known for his investigative writing, including the book that inspired the film "Philomena."
- 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633e809008190b0021d41b99f9700 |
completed | April 20, 2026, 2:10 p.m. |
Created at: April 10, 2026, 1:39 p.m.