Triple
T12069039
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Johnson v. Zerbst |
E287372
|
entity |
| Predicate | appliesToCases |
P1129
|
FINISHED |
| Object | serious criminal cases in federal court |
—
|
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: serious criminal cases in federal court | Statement: [Johnson v. Zerbst, appliesToCases, serious criminal cases in federal court]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliesToCases Context triple: [Johnson v. Zerbst, appliesToCases, serious criminal cases in federal court]
-
A.
appliesTo
chosen
Indicates that something is relevant, valid, or has effect in relation to a particular entity, case, or context.
-
B.
appliesAlsoTo
Indicates that a condition, rule, or characteristic that applies to one entity is additionally applicable to another entity.
-
C.
appliesFrom
Indicates that a rule, condition, or effect begins to be applicable starting from a specific point in time or state.
-
D.
appliesVia
Indicates that an action, rule, or effect is carried out, implemented, or achieved through a specified method, medium, or mechanism.
-
E.
appliesAcross
Indicates that a condition, rule, or property holds uniformly over multiple items, cases, or contexts.
- F. None of above.
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_69d6ab4846e081908ee7bbd66a6d3459 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9100b4ca8819084845ca4c13e34ce |
completed | April 10, 2026, 2:58 p.m. |
| PD | Predicate disambiguation | batch_69d902bda47c8190b94860b31df4a98c |
completed | April 10, 2026, 2:01 p.m. |
Created at: April 8, 2026, 9:48 p.m.