Triple
T6328750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dragomir Mrsic |
E141922
|
entity |
| Predicate | laterCareerChange |
P19158
|
FINISHED |
| Object | from criminal activity to acting |
—
|
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: from criminal activity to acting | Statement: [Dragomir Mrsic, laterCareerChange, from criminal activity to acting]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: laterCareerChange Context triple: [Dragomir Mrsic, laterCareerChange, from criminal activity to acting]
-
A.
laterCareer
chosen
Indicates that the associated information or events pertain to a later stage or phase in an entity’s professional life or career trajectory.
-
B.
managedCareerOf
Indicates that one entity was responsible for overseeing, directing, or handling the professional career of another entity.
-
C.
careerImpact
Indicates how one entity influences or changes another entity’s professional trajectory, opportunities, or outcomes.
-
D.
careerStart
Indicates the point in time when an entity begins its professional career or main occupational activity.
-
E.
targetCareer
Indicates that one entity is the intended or pursued career or professional goal of another entity.
- 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_69c008d201748190917e69c41ba3f978 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0651197908190a30e504e2442d40f |
completed | March 22, 2026, 9:54 p.m. |
| PD | Predicate disambiguation | batch_69c060e7e2d48190af9d004236466788 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:29 p.m.