Triple
T10699805
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rensselaer Russell Nelson |
E252242
|
entity |
| Predicate | legalEducationMethod |
P9439
|
FINISHED |
| Object | reading 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: reading law | Statement: [Rensselaer Russell Nelson, legalEducationMethod, reading law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalEducationMethod Context triple: [Rensselaer Russell Nelson, legalEducationMethod, reading law]
-
A.
legalTraining
chosen
Indicates that one entity has provided or received education or instruction in law from another entity.
-
B.
legalSchoolContrastedWith
Indicates a contrast or opposition drawn between two legal schools, highlighting their differing principles, methods, or doctrines.
-
C.
legalEducationRequiredForPractice
Indicates that a specific type or level of legal education is required as a prerequisite for engaging in legal practice.
-
D.
legalTraditionsTaught
Indicates that one entity teaches, covers, or includes specific legal traditions in its instruction or curriculum.
-
E.
legalSchoolPractice
Indicates that a particular legal practice, method, or approach is characteristic of, endorsed by, or derived from a specific school or tradition 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_69d6aa5cbabc8190973e683950d89faf |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fd8abd7c81909c274aa1699a3695 |
completed | April 9, 2026, 1:14 a.m. |
| PD | Predicate disambiguation | batch_69d6dd8cc0788190b4c02a772e4b58b3 |
completed | April 8, 2026, 10:58 p.m. |
Created at: April 8, 2026, 9:12 p.m.