Triple
T1169808
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diagnostic Radiology Residency |
E24888
|
entity |
| Predicate | includesTrainingIn |
P23937
|
FINISHED |
| Object | radiography |
—
|
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: radiography | Statement: [Diagnostic Radiology Residency, includesTrainingIn, radiography]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesTrainingIn Context triple: [Diagnostic Radiology Residency, includesTrainingIn, radiography]
-
A.
requiresTraining
Indicates that one entity can only be properly or legitimately used, performed, or engaged with if the other entity has first received appropriate training.
-
B.
trainingDataIncludes
Indicates that one entity’s training dataset contains or incorporates the other entity as part of its data.
-
C.
hasBeginnerFriendlyTraining
Indicates that an entity provides training or instructional resources suitable for beginners or those with little prior experience.
-
D.
trainingInstitution
Indicates that one entity serves as the institution or organization where another entity receives training or education.
-
E.
trainingComponent
chosen
Indicates that one entity functions as a training-related part, module, or element within a larger training process or system involving another entity.
- 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_69a494082a7c819095004f423f294a64 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bce821b481908bc278a3fa7973f4 |
completed | March 1, 2026, 10:25 p.m. |
| PD | Predicate disambiguation | batch_69a4bb5656948190b0b1d5446ad06005 |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:45 p.m.