Triple
T14229965
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | neuroepithelial cyst |
E352725
|
entity |
| Predicate | hasImagingFeature |
P67713
|
FINISHED |
| Object | well-circumscribed cystic lesion |
—
|
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: well-circumscribed cystic lesion | Statement: [neuroepithelial cyst, hasImagingFeature, well-circumscribed cystic lesion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasImagingFeature Context triple: [neuroepithelial cyst, hasImagingFeature, well-circumscribed cystic lesion]
-
A.
hasImagingType
Indicates the specific imaging modality or technique associated with or used in a given imaging procedure or result.
-
B.
hasPhotoFeature
Indicates that an entity possesses a characteristic, capability, or option specifically related to photos or photography.
-
C.
hasAIPhotoFeatures
Indicates that an entity provides or supports photo-related features powered by artificial intelligence.
-
D.
hasCamera
Indicates that an entity is equipped with or possesses a camera.
-
E.
hasImagingFinding
chosen
Indicates that an entity (typically a patient, case, or anatomical region) is associated with a specific observation or result identified through an imaging procedure (e.g., X-ray, CT, MRI).
- 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_69d8278adc7c8190a9218d69bce3c4e6 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de622b89fc8190af08dab9e1976759 |
completed | April 14, 2026, 3:50 p.m. |
| PD | Predicate disambiguation | batch_69de05bf069c8190b69f00f00f5eb126 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 10, 2026, 1:07 a.m.