Triple
T4549367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nicola Romeo |
E110124
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Nicola Romeo |
E110124
|
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: Nicola Romeo | Statement: [Nicola Romeo, name, Nicola Romeo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nicola Romeo Context triple: [Nicola Romeo, name, Nicola Romeo]
-
A.
Nicola Romeo
chosen
Nicola Romeo was an Italian engineer and entrepreneur best known for taking over and transforming the car manufacturer that became Alfa Romeo into a prominent automotive brand.
-
B.
Gabriele Capone
Gabriele Capone was an Italian immigrant barber and the father of notorious American gangster Al Capone.
-
C.
Leo Rossi
Leo Rossi is an American character actor known for his supporting roles in crime dramas and thrillers in film and television.
-
D.
Marco Barricelli
Marco Barricelli is an Italian-American actor and theatre director known for his work on stage and in voice roles for film and television.
-
E.
Bruno Siciliano
Bruno Siciliano is an Italian roboticist and professor renowned for his influential research, leadership, and educational contributions in the field of robotics and automation.
- 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_69bd4412524c8190be5bcc9ddee91848 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd57f3f8348190868e274ac4df87ce |
completed | March 20, 2026, 2:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be03391b3481909fd41ac03abe5d1b |
completed | March 21, 2026, 2:32 a.m. |
Created at: March 20, 2026, 1:05 p.m.