Triple
T13131270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Galenic medicine |
E311969
|
entity |
| Predicate | interpretsDiseaseAs |
P108226
|
FINISHED |
| Object | imbalance of humors |
—
|
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: imbalance of humors | Statement: [Galenic medicine, interpretsDiseaseAs, imbalance of humors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: interpretsDiseaseAs Context triple: [Galenic medicine, interpretsDiseaseAs, imbalance of humors]
-
A.
humanDisease
Indicates that the subject is a disease that affects humans.
-
B.
diseaseType
Indicates that one entity is classified as a specific type or category of disease in relation to another entity.
-
C.
includesDisease
Indicates that one entity (such as a category, group, or collection) contains or encompasses a particular disease as part of its members or elements.
-
D.
diagnosedWith
Indicates that a subject has been identified, typically by a medical professional, as having a particular disease or medical condition.
-
E.
settingOfIllness
Indicates the context, environment, or circumstances in which an illness occurs or manifests.
- 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_69d806a9fe888190b081e2d9ea665d6c |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d981b27a8c81909a92ab7be5d3a7e9 |
completed | April 10, 2026, 11:03 p.m. |
| PD | Predicate disambiguation | batch_69d98043a74c81908648e6cd0b4c7f71 |
completed | April 10, 2026, 10:57 p.m. |
| PDg | Predicate description generation | batch_69d98134df64819084a5674f9475dcc2 |
completed | April 10, 2026, 11:01 p.m. |
Created at: April 9, 2026, 9:08 p.m.