Triple
T29024503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Korey Wise |
E737550
|
entity |
| Predicate | legalStatusAtConviction |
P194072
|
FINISHED |
| Object | tried and sentenced as an adult |
—
|
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: tried and sentenced as an adult | Statement: [Korey Wise, legalStatusAtConviction, tried and sentenced as an adult]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalStatusAtConviction Context triple: [Korey Wise, legalStatusAtConviction, tried and sentenced as an adult]
-
A.
criminalStatus
Indicates the legal condition of an entity with respect to criminal law, such as whether they are accused, convicted, or cleared of a crime.
-
B.
convictionStatusInOriginalTrial
Indicates whether an entity was found guilty or not guilty in the initial (original) court trial.
-
C.
convictedOf
Indicates that a person or entity has been found guilty of committing a specified offense or crime through a formal legal process.
-
D.
legalStatusAtIssue
Indicates that the legal status of an entity is the central subject of dispute, consideration, or determination in a legal context.
-
E.
hasHadCriminalConviction
Indicates that an entity has previously been found guilty of a criminal offense through a legal process.
- 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_69f077ef00fc81909325f084ad37c035 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69fd5f29b1988190877764ef2a399c7f |
completed | May 8, 2026, 3:57 a.m. |
| PD | Predicate disambiguation | batch_69fd5e30194c819085b5ce586122ab37 |
completed | May 8, 2026, 3:53 a.m. |
| PDg | Predicate description generation | batch_69fd5f2903a48190ac4b718bff99c6cf |
completed | May 8, 2026, 3:57 a.m. |
Created at: April 28, 2026, 9:51 a.m.