Triple
T32742093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Udo Proksch |
E837242
|
entity |
| Predicate | hasCriminalPenalty |
P185670
|
FINISHED |
| Object | long-term imprisonment |
—
|
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: long-term imprisonment | Statement: [Udo Proksch, hasCriminalPenalty, long-term imprisonment]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCriminalPenalty Context triple: [Udo Proksch, hasCriminalPenalty, long-term imprisonment]
-
A.
hasHadCriminalConviction
Indicates that an entity has previously been found guilty of a criminal offense through a legal process.
-
B.
hasCriminalCharacter
Indicates that an entity possesses traits, behaviors, or a reputation associated with criminal activity or unlawful conduct.
-
C.
hasFirstConviction
Indicates that an entity has received its first legal conviction for an offense.
-
D.
hasLawEnforcementHistory
Indicates that an entity has a record of past involvement with law enforcement, such as prior incidents, investigations, or offenses.
-
E.
hasCriminalElement
Indicates that the subject involves, contains, or is associated with an illegal or criminal component, activity, or characteristic.
- 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_69f34936e1748190b797e406e4e9293a |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f7c33d59808190b647989a093f3488 |
completed | May 3, 2026, 9:50 p.m. |
| PD | Predicate disambiguation | batch_69f7c1b6e7a881908deb96bedb2713f4 |
completed | May 3, 2026, 9:44 p.m. |
| PDg | Predicate description generation | batch_69f7c29cf36481908e472d4dcb5573b9 |
completed | May 3, 2026, 9:48 p.m. |
Created at: May 1, 2026, 1:12 a.m.