Triple
T96329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Academy of Medicine |
E1939
|
entity |
| Predicate | topicOfWork |
P450
|
FINISHED |
| Object | health care quality |
—
|
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: health care quality | Statement: [National Academy of Medicine, topicOfWork, health care quality]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: topicOfWork Context triple: [National Academy of Medicine, topicOfWork, health care quality]
-
A.
fieldOfWork
Indicates the professional or academic domain in which an entity is primarily engaged or specializes.
-
B.
typeOfWork
Indicates the kind or category of work associated with or performed by an entity.
-
C.
associatedWork
Indicates that there exists a related or connected work (such as a publication, creative piece, or project) that is meaningfully linked to the subject.
-
D.
subjectMatter
chosen
Indicates the topic, theme, or content area that something (such as a work, document, or discussion) is about.
-
E.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
- 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_69a24d4862f881908cc8b89d3a78031d |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a250cb400c8190b56343bbe19b48c7 |
completed | Feb. 28, 2026, 2:19 a.m. |
| PD | Predicate disambiguation | batch_69a24ebd19c48190bab291fea0ecc0c2 |
completed | Feb. 28, 2026, 2:11 a.m. |
Created at: Feb. 28, 2026, 2:09 a.m.