Triple
T23861727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marco Buschmann |
E592467
|
entity |
| Predicate | areaOfLegalPractice |
P6403
|
FINISHED |
| Object | German law |
—
|
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: German law | Statement: [Marco Buschmann, areaOfLegalPractice, German law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areaOfLegalPractice Context triple: [Marco Buschmann, areaOfLegalPractice, German law]
-
A.
legalPractice
Indicates a relationship where an entity engages in or is associated with the professional provision of legal services or the practice of law.
-
B.
practicedLawIn
Indicates that a person engaged in the professional practice of law within a specified jurisdiction or location.
-
C.
legalArea
Indicates the specific field or branch of law that a legal matter, case, or document pertains to.
-
D.
branchOfLaw
chosen
Indicates a relationship where one legal field or discipline is a subdivision or specialized area within a broader body of law.
-
E.
notableAreaOfLaw
Indicates that a person or entity is particularly recognized or distinguished in a specific field or area of law.
- 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_69e25d22eb488190914b193aff952e83 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1cae0b4bc819089f491d9e817d160 |
completed | April 29, 2026, 9:09 a.m. |
| PD | Predicate disambiguation | batch_69f1614a65a88190bde1efb368a151e4 |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 8:13 p.m.