Triple
T6518973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rossi |
E148330
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Massimo Rossi
Massimo Rossi is an Italian footballer known for his career as a midfielder in Italian professional leagues.
|
E612985
|
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: Massimo Rossi | Statement: [Rossi, hasNotableBearer, Massimo Rossi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Massimo Rossi Context triple: [Rossi, hasNotableBearer, Massimo Rossi]
-
A.
Sergio Rossi
Sergio Rossi is an Italian luxury footwear brand renowned for its high-end, handcrafted women’s shoes and elegant design.
-
B.
Alessandro Antonelli
Alessandro Antonelli was a 19th-century Italian architect best known for designing Turin’s iconic Mole Antonelliana.
-
C.
Stefano Pessina
Stefano Pessina is an Italian-Monegasque billionaire businessman best known as the longtime leader and major shareholder behind the global pharmacy and retail group Walgreens Boots Alliance.
-
D.
Stefano Arnaldi
Stefano Arnaldi is a composer best known for creating the musical score for the film "Tea with Mussolini."
-
E.
Filippo Barigioni
Filippo Barigioni was an Italian Baroque architect and sculptor active in Rome in the early 18th century, known for his work on churches, fountains, and urban spaces.
- 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: Massimo Rossi Triple: [Rossi, hasNotableBearer, Massimo Rossi]
Generated description
Massimo Rossi is an Italian footballer known for his career as a midfielder in Italian professional leagues.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Massimo Rossi Target entity description: Massimo Rossi is an Italian footballer known for his career as a midfielder in Italian professional leagues.
-
A.
Sergio Rossi
Sergio Rossi is an Italian luxury footwear brand renowned for its high-end, handcrafted women’s shoes and elegant design.
-
B.
Alessandro Antonelli
Alessandro Antonelli was a 19th-century Italian architect best known for designing Turin’s iconic Mole Antonelliana.
-
C.
Stefano Pessina
Stefano Pessina is an Italian-Monegasque billionaire businessman best known as the longtime leader and major shareholder behind the global pharmacy and retail group Walgreens Boots Alliance.
-
D.
Stefano Arnaldi
Stefano Arnaldi is a composer best known for creating the musical score for the film "Tea with Mussolini."
-
E.
Filippo Barigioni
Filippo Barigioni was an Italian Baroque architect and sculptor active in Rome in the early 18th century, known for his work on churches, fountains, and urban spaces.
- 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_69c687e68e748190baceb9298f32d3ed |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6ac11d0e481908103c4b51de9521e |
completed | March 27, 2026, 4:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c70064bfa48190bbb5b4f92dde8dde |
completed | March 27, 2026, 10:10 p.m. |
| NEDg | Description generation | batch_69c702c7fbf88190b8ef07227cb51f77 |
completed | March 27, 2026, 10:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c70357502c8190b9e7990c44a44bcc |
completed | March 27, 2026, 10:23 p.m. |
Created at: March 27, 2026, 1:44 p.m.