Triple
T9606905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trey Gowdy |
E231993
|
entity |
| Predicate | licensedToPractice |
P89206
|
FINISHED |
| Object | law in South Carolina |
—
|
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: law in South Carolina | Statement: [Trey Gowdy, licensedToPractice, law in South Carolina]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: licensedToPractice Context triple: [Trey Gowdy, licensedToPractice, law in South Carolina]
-
A.
requiresLicenseForPractice
Indicates that engaging in the specified professional practice is legally contingent upon holding a valid license.
-
B.
approvedPractice
Indicates that a particular practice, method, or procedure has been formally reviewed and granted official approval for use or adoption.
-
C.
hasLanguageOfPractice
Indicates that an entity uses or operates in a particular language as its regular or primary medium of practice.
-
D.
medicalQualificationFrom
Indicates that a person or medical professional obtained their medical qualification or degree from a specified institution or source.
-
E.
rulesOfPractice
Indicates the formal procedures, standards, or guidelines that govern how a particular activity, profession, or process must be conducted.
- F. None of above. chosen
Provenance (4 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_69ca8485a90c819094fe40b42fde9d70 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9a62372881908bf21be91e7285fb |
completed | April 1, 2026, 10:21 p.m. |
| PD | Predicate disambiguation | batch_69ccd5a6fd2481908efd131e207b8143 |
completed | April 1, 2026, 8:21 a.m. |
| PDg | Predicate description generation | batch_69ccd93fc45c8190a823305e461e581d |
completed | April 1, 2026, 8:37 a.m. |
Created at: March 30, 2026, 8:08 p.m.