Triple
T7397457
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scopes v. State |
E170656
|
entity |
| Predicate | sentencingError |
P76352
|
FINISHED |
| Object | judge set fine instead of jury |
—
|
LITERAL 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: judge set fine instead of jury | Statement: [Scopes v. State, sentencingError, judge set fine instead of jury]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sentencingError Context triple: [Scopes v. State, sentencingError, judge set fine instead of jury]
-
A.
sentencedTo
Indicates that an authority has officially assigned a specific punishment or penalty to an entity, typically as the outcome of a legal or disciplinary process.
-
B.
sentencedOn
Indicates that a judicial authority has formally imposed a legal sentence or punishment on an entity on a specific date.
-
C.
createdSentencingDisparityBetween
Indicates that one entity’s actions or decisions caused or contributed to an unequal or inconsistent sentencing outcome between two or more parties.
-
D.
throws
Indicates that one entity propels or hurls another entity or object through space, typically by a deliberate physical action.
-
E.
errorTerm
Indicates the specific discrepancy or residual value that quantifies the difference between an observed outcome and its predicted or true value in a model or calculation.
- F. None of above. chosen
Provenance (4 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_69c68a5f04188190ac266569c9280347 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f24abcd08190b8428fa22b2fbd4f |
completed | March 27, 2026, 9:10 p.m. |
| PD | Predicate disambiguation | batch_69c6f0309cc88190b55d278969400294 |
completed | March 27, 2026, 9:01 p.m. |
| PDg | Predicate description generation | batch_69c6f0be2b1c8190bea06100a7caef2b |
completed | March 27, 2026, 9:03 p.m. |
Created at: March 27, 2026, 3:09 p.m.