Triple
T14581578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Franco Nero |
E342204
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object | Carlo Gabriel Nero |
E331538
|
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: Carlo Gabriel Nero | Statement: [Franco Nero, child, Carlo Gabriel Nero]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carlo Gabriel Nero Context triple: [Franco Nero, child, Carlo Gabriel Nero]
-
A.
Carlo Gabriel Nero
chosen
Carlo Gabriel Nero is an Italian-British film director and screenwriter known for his work on independent films and for being part of a prominent acting family.
-
B.
Luciano
Luciano is a masculine given name of Italian origin, famously borne by the renowned operatic tenor Luciano Pavarotti.
-
C.
Antonio Contino
Antonio Contino was a Venetian architect best known for designing the iconic Bridge of Sighs in Venice, Italy.
-
D.
Guido Caroli
Guido Caroli was an Italian speed skater best known for lighting the Olympic cauldron at the 1956 Winter Olympics in Cortina d'Ampezzo.
-
E.
Leo Catozzo
Leo Catozzo was an Italian film editor and inventor best known for his influential work in post-war Italian cinema and for creating the Catozzo splicing machine used worldwide in film editing.
- 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_69d822ddc0f081909cd8163c7de298cd |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb41e71748190a1deacc819dd26d3 |
completed | April 14, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd94ba235c81909e0408ccf2be9628 |
completed | May 8, 2026, 7:46 a.m. |
Created at: April 10, 2026, 1:24 a.m.