Triple
T20029044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ever After |
E495070
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Roger Bondelli |
—
|
NE NERFINISHED |
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: Roger Bondelli | Statement: [Ever After, editedBy, Roger Bondelli]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roger Bondelli Context triple: [Ever After, editedBy, Roger Bondelli]
-
A.
Roger Bondelli
chosen
Roger Bondelli is a film editor best known for his work on the 1996 historical adventure thriller "The Ghost and the Darkness."
-
B.
Alberto Colantuoni
Alberto Colantuoni was an Italian literary figure best known for establishing the prestigious Viareggio Prize for literature.
-
C.
Giovanni Antonelli
Giovanni Antonelli was an Italian astronomer and Jesuit priest known for his contributions to 19th-century astronomical research and observatory work.
-
D.
Enzo Villani
Enzo Villani is a business executive and entrepreneur known for his leadership roles in fintech, blockchain, and digital asset investment firms.
-
E.
Sergio Fantoni
Sergio Fantoni was an Italian actor known for his work in mid-20th-century cinema and television, including prominent roles in international war and drama films.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da626bfd288190aa5d65098b6433ae |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e662908df081909a6c8ccf0dd90fff |
completed | April 20, 2026, 5:29 p.m. |
Created at: April 11, 2026, 3:36 p.m.