Triple
T13700857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La cena di Natale |
E328511
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Nicola Nocella
Nicola Nocella is an Italian actor known for his roles in contemporary Italian cinema and television comedies and dramas.
|
E1066399
|
NE FINISHED |
How this triple was built (4 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 Nocella | Statement: [La cena di Natale, hasCastMember, Nicola Nocella]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nicola Nocella Context triple: [La cena di Natale, hasCastMember, Nicola Nocella]
-
A.
Nicola Antonelli
Nicola Antonelli is an individual notable enough to be recognized as a bearer of the Italian surname Antonelli.
-
B.
Nicola Villani
Nicola Villani is an Italian mathematician and academic known for his contributions to mathematical analysis and related fields.
-
C.
Nicola De Martino
Nicola De Martino is an individual notable enough to be recognized as a bearer of the surname De Martino.
-
D.
Nicola Romeo
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.
-
E.
Nicola Giuliano
Nicola Giuliano is an Italian film producer best known for his work on acclaimed auteur-driven films, including the Oscar-winning "The Great Beauty."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Nicola Nocella Triple: [La cena di Natale, hasCastMember, Nicola Nocella]
Generated description
Nicola Nocella is an Italian actor known for his roles in contemporary Italian cinema and television comedies and dramas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nicola Nocella Target entity description: Nicola Nocella is an Italian actor known for his roles in contemporary Italian cinema and television comedies and dramas.
-
A.
Nicola Antonelli
Nicola Antonelli is an individual notable enough to be recognized as a bearer of the Italian surname Antonelli.
-
B.
Nicola Villani
Nicola Villani is an Italian mathematician and academic known for his contributions to mathematical analysis and related fields.
-
C.
Nicola De Martino
Nicola De Martino is an individual notable enough to be recognized as a bearer of the surname De Martino.
-
D.
Nicola Romeo
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.
-
E.
Nicola Giuliano
Nicola Giuliano is an Italian film producer best known for his work on acclaimed auteur-driven films, including the Oscar-winning "The Great Beauty."
- F. None of above. chosen
Provenance (5 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_69d8076ff62081908a7bd79889edd7a0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc879adc88190b03f1cf815b71061 |
completed | April 12, 2026, 4:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c0db310c81909c22507f9a3e7dee |
completed | May 3, 2026, 9:40 p.m. |
| NEDg | Description generation | batch_69f7c1e73fb481909f89ab3c0e9fb7d0 |
completed | May 3, 2026, 9:45 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7c33c2f34819084502d5f03f09ddd |
completed | May 3, 2026, 9:50 p.m. |
Created at: April 9, 2026, 9:54 p.m.