Triple
T27875511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marius Josipović |
E704918
|
entity |
| Predicate | hasCriminalActivity |
P143570
|
FINISHED |
| Object | fraud |
—
|
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: fraud | Statement: [Marius Josipović, hasCriminalActivity, fraud]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCriminalActivity Context triple: [Marius Josipović, hasCriminalActivity, fraud]
-
A.
hasCriminalCharacter
Indicates that an entity possesses traits, behaviors, or a reputation associated with criminal activity or unlawful conduct.
-
B.
hasHadCriminalConviction
Indicates that an entity has previously been found guilty of a criminal offense through a legal process.
-
C.
hasCriminalElement
chosen
Indicates that the subject involves, contains, or is associated with an illegal or criminal component, activity, or characteristic.
-
D.
hasLawEnforcementHistory
Indicates that an entity has a record of past involvement with law enforcement, such as prior incidents, investigations, or offenses.
-
E.
hasFirstConviction
Indicates that an entity has received its first legal conviction for an offense.
- 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_69ef84111bb4819084298f994b31c62f |
completed | April 27, 2026, 3:43 p.m. |
| NER | Named-entity recognition | batch_69f6afebd7ec8190ab696f363d84abf0 |
completed | May 3, 2026, 2:16 a.m. |
| PD | Predicate disambiguation | batch_69f6aca204148190850a3dc325bc07b7 |
completed | May 3, 2026, 2:02 a.m. |
Created at: April 27, 2026, 6:27 p.m.