Triple
T22144551
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Francesco Bagnaia |
E547251
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Francesco |
—
|
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: Francesco | Statement: [Francesco Bagnaia, givenName, Francesco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Francesco Context triple: [Francesco Bagnaia, givenName, Francesco]
-
A.
Francesco
Francesco is the Italian given name of Frank Nitti, a notorious American mobster and key figure in Al Capone’s Chicago Outfit.
-
B.
Francesco
Francesco is the given name of Italian actor Franco Nero, renowned for his iconic role in the Spaghetti Western film "Django."
-
C.
Francesco
Francesco is the birth name of Frank Capra, the renowned Italian-American film director known for classic Hollywood movies such as "It's a Wonderful Life."
-
D.
Francesco
chosen
Francesco is a masculine given name of Italian origin, derived from the Latin Franciscus and commonly associated with figures such as Saint Francis of Assisi.
-
E.
Filippo
Filippo is an Italian given name most famously borne by former professional footballer and manager Filippo Inzaghi.
- 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_69e11e3a95d88190a3bd80d9471976c3 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f129efd52c8190ab0acff5bbfc0d77 |
completed | April 28, 2026, 9:43 p.m. |
Created at: April 16, 2026, 8:33 p.m.