Triple
T22702867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Da Vinci Code |
E561368
|
entity |
| Predicate | author |
P4
|
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: [The Da Vinci Code, author, Dan Brown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Brown Context triple: [The Da Vinci Code, author, Dan Brown]
-
A.
Dan Brown
Dan Brown is an Australian musician best known as a guitarist for the metalcore band The Amity Affliction.
-
B.
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."
-
C.
Dan Brownlie
Dan Brownlie is an English football manager best known for managing non-league side Basingstoke Town F.C.
-
D.
Dante Brown
Dante Brown is an American actor known for his roles in film and television, including a starring role in the 2019 psychological horror film "Ma."
-
E.
Robert Ludlum
Robert Ludlum was an American author best known for his fast-paced espionage and thriller novels, including the Jason Bourne series.
- 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_69e2454e615481909c177440be559d2c |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f178cbf5788190bc8cd1bc71a861e5 |
completed | April 29, 2026, 3:19 a.m. |
Created at: April 17, 2026, 3:16 p.m.