Triple
T24852045
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Azdak |
E621913
|
entity |
| Predicate | legalStyle |
P160076
|
FINISHED |
| Object | morally driven |
—
|
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: morally driven | Statement: [Azdak, legalStyle, morally driven]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalStyle Context triple: [Azdak, legalStyle, morally driven]
-
A.
legalStyle
chosen
Indicates that something conforms to, is expressed in, or is characterized by a legal or law-related style or manner.
-
B.
legalDoctrine
Indicates that one legal principle, rule, or theory is being applied, referenced, or relied upon as an authoritative basis for interpreting or deciding a legal issue.
-
C.
legalContext
Indicates that the relationship or action occurs within, is shaped by, or is relevant to a specific legal framework, proceeding, or set of legal norms.
-
D.
languageInLaw
Indicates that a specific language is used as the official or operative language within a particular law or legal document.
-
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_69e2fac297e481909d3aedc75f585e42 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f60c3b09488190ade1b69ff7f0df0e |
completed | May 2, 2026, 2:37 p.m. |
| PD | Predicate disambiguation | batch_69f60b8461ac81908c5bd3d73eed59f4 |
completed | May 2, 2026, 2:34 p.m. |
Created at: April 18, 2026, 5:20 a.m.