Triple
T210399
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doctor of Dentistry (Dr. med. dent.) |
E4703
|
entity |
| Predicate | requiresLicenseForPractice |
P9144
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Doctor of Dentistry (Dr. med. dent.), requiresLicenseForPractice, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: requiresLicenseForPractice Context triple: [Doctor of Dentistry (Dr. med. dent.), requiresLicenseForPractice, yes]
-
A.
requiresPermitFor
Indicates that one entity must obtain formal permission or authorization before performing an action involving another entity.
-
B.
associatedWithPractice
Indicates a relationship in which an entity is connected or linked to a particular practice, activity, or customary way of doing something.
-
C.
usesLegalCode
Indicates that one entity applies, references, or operates under a particular legal code in its actions or regulations.
-
D.
license
Indicates that one entity has granted another entity formal permission or authorization to use, perform, or exploit something under specified terms.
-
E.
practicedLawIn
Indicates that a person engaged in the professional practice of law within a specified jurisdiction or location.
- 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_69a2575cb1dc8190a01ad332426dc339 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25d35aa288190966b6e15af1525cb |
completed | Feb. 28, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69a25b4f71b88190866c8262922ae204 |
completed | Feb. 28, 2026, 3:04 a.m. |
| PDg | Predicate description generation | batch_69a25d3463648190ac716d7475378536 |
completed | Feb. 28, 2026, 3:12 a.m. |
Created at: Feb. 28, 2026, 2:52 a.m.