Triple
T38230601
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Premio Strega |
E1012274
|
entity |
| Predicate | originalNumberOfJurors |
—
|
GENERATED |
| Object | 170 |
—
|
UNRECOGNIZED GENERATED |
How this triple was built (1 step)
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.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originalNumberOfJurors Context triple: [Premio Strega, originalNumberOfJurors, 170]
-
A.
hasNumberOfJurors
chosen
Indicates the relationship specifying how many jurors are associated with a given legal case, trial, or proceeding.
-
B.
jurorNumber
Indicates the specific numerical identifier assigned to a juror within a jury.
-
C.
hasJurors
Indicates that one entity serves as or includes jurors in relation to another entity, typically in the context of a legal case or proceeding.
-
D.
numberOfJudges
Indicates the total count of judges associated with a particular case, event, or entity.
-
E.
minimumNumberOfJustices
Indicates the smallest number of justices required for a court or judicial body to validly conduct its proceedings or make decisions.
- F. None of above.
Provenance (1 batch)
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_69f76dd25e0c81909f2abd0803e5e3ee |
completed | May 3, 2026, 3:46 p.m. |
Created at: May 3, 2026, 4:30 p.m.