Triple
T15631195
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sameen Shaw |
E375814
|
entity |
| Predicate | medicalBackground |
P119520
|
FINISHED |
| Object | trained as a physician |
—
|
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: trained as a physician | Statement: [Sameen Shaw, medicalBackground, trained as a physician]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: medicalBackground Context triple: [Sameen Shaw, medicalBackground, trained as a physician]
-
A.
medicalText
Indicates that the subject is a piece of text whose content is medical in nature, such as clinical, diagnostic, or health-related information.
-
B.
medicalPractice
Indicates a relationship where an entity engages in or carries out the professional provision of medical care or services.
-
C.
clinicalBaseOf
Indicates that one clinical entity serves as the foundational basis or underlying source for another clinical entity (such as a diagnosis, assessment, or decision).
-
D.
clinicalEmphasis
Indicates a focus or priority placed on clinical aspects, practices, or outcomes within a given context or activity.
-
E.
medicalEvent
Indicates that a specific health-related occurrence or clinical incident has taken place involving one or more entities.
- 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_69d85cd035a48190b73d5579ab73969a |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04eb536348190b93ed3c178d1ffb8 |
completed | April 16, 2026, 2:51 a.m. |
| PD | Predicate disambiguation | batch_69deda868d4481908f4bce1c64d2902a |
completed | April 15, 2026, 12:23 a.m. |
| PDg | Predicate description generation | batch_69dff7f3016c8190ac68d76e65e07af4 |
completed | April 15, 2026, 8:41 p.m. |
Created at: April 10, 2026, 4:14 a.m.