Triple
T15236631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Applied Health Sciences |
E364142
|
entity |
| Predicate | appliesKnowledgeFrom |
P33840
|
FINISHED |
| Object | health sciences |
—
|
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 sciences | Statement: [Applied Health Sciences, appliesKnowledgeFrom, health sciences]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliesKnowledgeFrom Context triple: [Applied Health Sciences, appliesKnowledgeFrom, health sciences]
-
A.
usesKnowledgeOf
chosen
Indicates that one entity applies or draws upon the knowledge possessed by another entity in performing an action or achieving a result.
-
B.
appliesTheory
Indicates that an entity uses or implements a particular theory in analyzing, explaining, or addressing something.
-
C.
appliesFrom
Indicates that a rule, condition, or effect begins to be applicable starting from a specific point in time or state.
-
D.
appliesVia
Indicates that an action, rule, or effect is carried out, implemented, or achieved through a specified method, medium, or mechanism.
-
E.
appliesResearchTo
Indicates that an entity uses or implements research findings, methods, or insights in relation to another entity, context, or problem.
- 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_69d85a0ce24c81909c4d3b6475548c95 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e007d91e4881908ea52d11a3d4480a |
completed | April 15, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69deca899d5c8190be4a7c71e1683c69 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:12 a.m.