Triple
T1756189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeremiah Sullivan Black |
E38552
|
entity |
| Predicate | admittedToPractice |
P2755
|
FINISHED |
| Object | Pennsylvania bar |
—
|
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: Pennsylvania bar | Statement: [Jeremiah Sullivan Black, admittedToPractice, Pennsylvania bar]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: admittedToPractice Context triple: [Jeremiah Sullivan Black, admittedToPractice, Pennsylvania bar]
-
A.
practicedLawIn
chosen
Indicates that a person engaged in the professional practice of law within a specified jurisdiction or location.
-
B.
requiresLicenseForPractice
Indicates that engaging in the specified professional practice is legally contingent upon holding a valid license.
-
C.
associatedWithPractice
Indicates a relationship in which an entity is connected or linked to a particular practice, activity, or customary way of doing something.
-
D.
clerkship
Indicates a professional training relationship in which one person serves as a clerk or apprentice under the supervision of another, typically to gain practical experience.
-
E.
admittedState
Indicates that an entity has been formally accepted or allowed into a particular state or condition.
- 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_69a8862bdb2081908aefe831c8aa8017 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aba6a63f588190b53b39c6b97d74f4 |
completed | March 7, 2026, 4:16 a.m. |
| PD | Predicate disambiguation | batch_69aa61c7ef4c8190abec87c96a787d82 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:31 p.m.