Triple
T5651740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norwegian Code of 1687 |
E124526
|
entity |
| Predicate | legalAreasCovered |
P2167
|
FINISHED |
| Object | criminal 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: criminal law | Statement: [Norwegian Code of 1687, legalAreasCovered, criminal law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalAreasCovered Context triple: [Norwegian Code of 1687, legalAreasCovered, criminal law]
-
A.
legalTopicCoverage
Indicates that one entity (such as a document, service, or resource) addresses, discusses, or is relevant to a particular legal topic or area of law.
-
B.
legalArea
chosen
Indicates the specific field or branch of law that a legal matter, case, or document pertains to.
-
C.
jurisdictionCovered
Indicates that a particular jurisdiction or legal authority is included within the scope or coverage of another entity, rule, or arrangement.
-
D.
legalScope
Indicates the range, boundaries, or extent of authority, applicability, or effect that something has under a particular legal framework or rule.
-
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_69c00825df388190a58742fa9b1aa33d |
completed | March 22, 2026, 3:17 p.m. |
| NER | Named-entity recognition | batch_69c022d6af9481909eaeead2a39525ce |
completed | March 22, 2026, 5:11 p.m. |
| PD | Predicate disambiguation | batch_69c01b2274b48190b2ef57ed728f785c |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:42 p.m.