Triple
T583310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anaxagoras |
E15102
|
entity |
| Predicate | legalIssues |
P4511
|
FINISHED |
| Object | prosecuted for impiety in Athens |
—
|
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: prosecuted for impiety in Athens | Statement: [Anaxagoras, legalIssues, prosecuted for impiety in Athens]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalIssues Context triple: [Anaxagoras, legalIssues, prosecuted for impiety in Athens]
-
A.
legalCase
Indicates a relationship where a formal legal dispute or proceeding exists between parties, typically adjudicated by a court or similar authority.
-
B.
hasLegalIssue
chosen
Indicates that an entity is involved in, associated with, or subject to a legal problem, dispute, or proceeding.
-
C.
legalRepresentation
Indicates that one entity formally acts on behalf of another in legal matters, such as providing counsel, advocacy, or defense within a legal system.
-
D.
legalAct
Indicates that an entity performs, enacts, or is involved in a formal legal action, measure, or proceeding under a legal framework.
-
E.
legalConcept
Indicates a relationship where something is classified or treated as a concept defined and governed by law or legal theory.
- 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_69a4935783b8819082b77726ec10cc42 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49b8745c88190af9672e5fe8396c3 |
completed | March 1, 2026, 8:03 p.m. |
| PD | Predicate disambiguation | batch_69a494c9315c8190a773e8e00737d8a0 |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:33 p.m.