Triple
T21946236
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Silver Linings Playbook |
E541939
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | Matthew Quick |
—
|
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: Matthew Quick | Statement: [The Silver Linings Playbook, author, Matthew Quick]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matthew Quick Context triple: [The Silver Linings Playbook, author, Matthew Quick]
-
A.
Matthew Quick
chosen
Matthew Quick is an American novelist best known for writing "The Silver Linings Playbook," which was adapted into an Academy Award–winning film.
-
B.
Adam Haslett
Adam Haslett is an American author and journalist known for his critically acclaimed fiction exploring themes of mental illness, family, and contemporary politics.
-
C.
Greg Grabianski
Greg Grabianski is a screenwriter best known for his work on the parody horror-comedy film "Scary Movie 2."
-
D.
Paul Harding
Paul Harding is an American novelist best known for his Pulitzer Prize–winning debut novel "Tinkers."
-
E.
Paul Harding
Paul Harding is a British drum and bass producer and DJ best known as one half of the influential duo Pendulum.
- 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_69e0c47ef0e48190a50e1bcc43f4b3fd |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f12427c2b48190949c41bd3be2d9f3 |
completed | April 28, 2026, 9:18 p.m. |
Created at: April 16, 2026, 7:57 p.m.