Triple
T10677093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nino Rota |
E251647
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Nino |
E251647
|
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: Nino | Statement: [Nino Rota, givenName, Nino]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nino Context triple: [Nino Rota, givenName, Nino]
-
A.
Nino
chosen
Nino is the commonly used name of Italian composer Nino Rota, renowned for his film scores including those for Federico Fellini and The Godfather.
-
B.
Nino
Nino is a fictional Georgian noblewoman and one of the two tragic lovers in Kurban Said’s novel "Ali and Nino," commemorated by the moving sculpture "Ali and Nino" in Batumi, Georgia.
-
C.
Nanni
Nanni is an Italian given name commonly used as a familiar or diminutive form of Giovanni.
-
D.
Giannino
Giannino is an Italian given name, commonly used as a familiar or affectionate form of Giovanni.
-
E.
Nina
Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
- 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_69d6aa5b0d2881909584b20efc5877f0 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fb9563908190a69cf4bd2c24fd2f |
completed | April 9, 2026, 1:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d9887e974c81908c4943339ea9a93f |
completed | April 10, 2026, 11:32 p.m. |
Created at: April 8, 2026, 9:09 p.m.