Triple
T21159092
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | P-Valley |
E521387
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object | Dante Di Loreto |
—
|
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: Dante Di Loreto | Statement: [P-Valley, executiveProducer, Dante Di Loreto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dante Di Loreto Context triple: [P-Valley, executiveProducer, Dante Di Loreto]
-
A.
Dante Di Loreto
chosen
Dante Di Loreto is a television and film producer best known for his work on the hit musical series "Glee" and other acclaimed projects.
-
B.
Enrico Dante
Enrico Dante was an Italian cardinal of the Roman Catholic Church known for his long service in the Vatican and his expertise in liturgy and ceremonial protocol.
-
C.
Lorenzo Balducci
Lorenzo Balducci is an Italian actor known for his work in film, television, and theater, including roles in popular Italian teen dramas.
-
D.
Dante Lavelli
Dante Lavelli was a Hall of Fame American football wide receiver best known for his prolific career with the Cleveland Browns in the 1940s and 1950s.
-
E.
Lorenzo De Stefano
Lorenzo De Stefano is a film editor best known for his work on the influential bodybuilding documentary "Pumping Iron."
- 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_69e0b50d1ea481909c07e63c3ead9316 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7252e9ef481908f4904c535f3da8b |
completed | April 21, 2026, 7:20 a.m. |
Created at: April 16, 2026, 2:59 p.m.