Triple
T4718430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vittoria Vetra |
E104704
|
entity |
| Predicate | creator |
P184
|
FINISHED |
| Object | Dan Brown |
E72333
|
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: Dan Brown | Statement: [Vittoria Vetra, creator, Dan Brown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Brown Context triple: [Vittoria Vetra, creator, 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.
Robert Ludlum
Robert Ludlum was an American author best known for his fast-paced espionage and thriller novels, including the Jason Bourne series.
-
C.
Anthony Horowitz
Anthony Horowitz is a British novelist and screenwriter best known for his Alex Rider spy novels and numerous television crime dramas.
-
D.
Frederick Forsyth
Frederick Forsyth is a British thriller writer renowned for his meticulously researched, politically charged novels such as "The Day of the Jackal."
-
E.
Robert Grace
Robert Grace was an early American civic leader and philanthropist known for his role in colonial Philadelphia’s public institutions and community organizations.
- 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_69bd43ec4a348190bc41afae43375e71 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd642779a08190b01e588d515cf498 |
completed | March 20, 2026, 3:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be108bc0048190aeea8674f75105e5 |
completed | March 21, 2026, 3:29 a.m. |
Created at: March 20, 2026, 1:18 p.m.