Triple
T22219858
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gert Postel |
E549179
|
entity |
| Predicate | hasNoFormalTrainingIn |
P147345
|
FINISHED |
| Object | medicine |
—
|
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: medicine | Statement: [Gert Postel, hasNoFormalTrainingIn, medicine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNoFormalTrainingIn Context triple: [Gert Postel, hasNoFormalTrainingIn, medicine]
-
A.
isTaughtInformallyIn
Indicates that something is taught or learned in an informal setting or context, rather than through formal instruction.
-
B.
receivedTrainingIn
Indicates that one entity has undergone or been provided with training or instruction in a particular field, skill, or subject associated with another entity.
-
C.
isTaughtFormally
Indicates that one entity provides structured, formal instruction or education to another entity.
-
D.
hasTrainedAt
Indicates that an entity has received training, education, or instruction at a specified place or institution.
-
E.
hasNoFormalSalary
Indicates that an entity does not receive a fixed, officially defined salary for their role or work.
- 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_69e11e403d6481909a94d0aaf157f6ef |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12b8fa3d081908db0a0556b009d8f |
completed | April 28, 2026, 9:50 p.m. |
| PD | Predicate disambiguation | batch_69e71b4dcc408190a30429fb08fcf39e |
completed | April 21, 2026, 6:38 a.m. |
| PDg | Predicate description generation | batch_69e723f65c5c8190a0ee3c539e5d0767 |
completed | April 21, 2026, 7:15 a.m. |
Created at: April 16, 2026, 8:37 p.m.