Triple
T210394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doctor of Dentistry (Dr. med. dent.) |
E4703
|
entity |
| Predicate | relatedDegree |
P5202
|
FINISHED |
| Object | Doctor of Medicine (Dr. med.) |
—
|
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: Doctor of Medicine (Dr. med.) | Statement: [Doctor of Dentistry (Dr. med. dent.), relatedDegree, Doctor of Medicine (Dr. med.)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedDegree Context triple: [Doctor of Dentistry (Dr. med. dent.), relatedDegree, Doctor of Medicine (Dr. med.)]
-
A.
hasDegree
Indicates that an entity possesses or has been awarded a specific academic or professional degree.
-
B.
relatedField
Indicates that one field, topic, or area of study is connected or relevant to another in subject matter or application.
-
C.
academicDegree
Indicates that an entity holds or has been awarded a specific academic degree.
-
D.
relatedTo
Indicates a general, non-specific relationship or association exists between two entities.
-
E.
moreDistantlyRelatedTo
chosen
Indicates that one entity is related to another by a more distant or indirect relationship compared to some closer reference relationship.
- 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_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. |
Created at: Feb. 28, 2026, 2:52 a.m.