Triple
T21944265
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Inferno (2016 film) |
E541895
|
entity |
| Predicate | authorOfSourceWork |
P2353
|
FINISHED |
| Object | Dan Brown |
—
|
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: Dan Brown | Statement: [Inferno (2016 film), authorOfSourceWork, Dan Brown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Brown Context triple: [Inferno (2016 film), authorOfSourceWork, Dan Brown]
-
A.
Dan Brown
chosen
Dan Brown is an American author best known for his fast-paced mystery thrillers that blend historical, religious, and conspiracy themes, including the bestselling novel "The Da Vinci Code."
-
B.
Dan Brownlie
Dan Brownlie is an English football manager best known for managing non-league side Basingstoke Town F.C.
-
C.
Robert Ludlum
Robert Ludlum was an American author best known for his fast-paced espionage and thriller novels, including the Jason Bourne series.
-
D.
Nicholas Evans
Nicholas Evans was a British author best known for his bestselling novel "The Horse Whisperer," which was adapted into a major film.
-
E.
Anthony Horowitz
Anthony Horowitz is a British novelist and screenwriter best known for his Alex Rider spy novels and numerous television crime dramas.
- 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_69e0c47e2e5c81909a7f74ce3de50911 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1242688988190a7b8f033c49368de |
completed | April 28, 2026, 9:18 p.m. |
Created at: April 16, 2026, 7:56 p.m.