Triple
T36461017
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | September Laws (1983) in Sudan |
E898286
|
entity |
| Predicate | legalChangeType |
P30689
|
FINISHED |
| Object | Islamization of criminal code |
—
|
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: Islamization of criminal code | Statement: [September Laws (1983) in Sudan, legalChangeType, Islamization of criminal code]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalChangeType Context triple: [September Laws (1983) in Sudan, legalChangeType, Islamization of criminal code]
-
A.
hasLegalChange
Indicates that an entity has undergone or is associated with a modification in its legal status, rights, obligations, or regulatory conditions.
-
B.
legalAmendment
chosen
Indicates a formal change or modification made to an existing law, regulation, or legal document.
-
C.
legalReformer
Indicates that an entity works to change, improve, or modernize laws or legal systems.
-
D.
legalStatusChangedIn
Indicates that an entity’s legal status was altered or reclassified within a specified jurisdiction, context, or time frame.
-
E.
typeOfChangeRegulated
Indicates that one entity specifies or controls the kind of change or modification that is allowed or governed in another entity or process.
- 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_69f76e57f08481908593bd0bc34581c8 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69ffc516d1908190b475f5a6156b0ca8 |
completed | May 9, 2026, 11:36 p.m. |
| PD | Predicate disambiguation | batch_69ffc4a946e08190b3535a5dc15ac484 |
completed | May 9, 2026, 11:35 p.m. |
Created at: May 3, 2026, 4:10 p.m.