Triple
T7340804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guernsey law |
E169240
|
entity |
| Predicate | publicLawArea |
P6403
|
FINISHED |
| Object | administrative law of Guernsey |
—
|
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: administrative law of Guernsey | Statement: [Guernsey law, publicLawArea, administrative law of Guernsey]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: publicLawArea Context triple: [Guernsey law, publicLawArea, administrative law of Guernsey]
-
A.
legalArea
Indicates the specific field or branch of law that a legal matter, case, or document pertains to.
-
B.
notableAreaOfLaw
Indicates that a person or entity is particularly recognized or distinguished in a specific field or area of law.
-
C.
legalJurisdiction
Indicates the legal authority or geographic area whose laws and courts have the power to govern, regulate, or adjudicate matters involving the related entities.
-
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.
legalBackground
Indicates that an entity has education, training, or experience related to law or the legal profession.
- 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_69c68a57710481909f0c1f3c6ebdb6f2 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f347f25081908e6086d4073295f5 |
completed | March 27, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69c6f028fd748190b2ea5c3081958a42 |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:04 p.m.