Triple
T31019829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kank-A |
E790421
|
entity |
| Predicate | usedForSymptom |
P112879
|
FINISHED |
| Object | soreness in mouth |
—
|
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: soreness in mouth | Statement: [Kank-A, usedForSymptom, soreness in mouth]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedForSymptom Context triple: [Kank-A, usedForSymptom, soreness in mouth]
-
A.
isUsedForSymptom
chosen
Indicates that something (such as a treatment, medication, or intervention) is employed to address, alleviate, or manage a particular symptom.
-
B.
diseaseUsed
Indicates that a particular disease is employed or utilized as a tool, model, or condition within a given context or process.
-
C.
symptom
Indicates that a particular condition, disease, or problem manifests through a specific observable sign or complaint.
-
D.
usedFor
Indicates that one entity serves a purpose, function, or role in accomplishing, enabling, or supporting another entity or activity.
-
E.
viewsSymptoms
Indicates that one entity observes or examines the symptoms associated with another entity.
- 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_69f224c811508190a7de096a5b1f5798 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f760a35b988190904e6267553ad2fe |
completed | May 3, 2026, 2:50 p.m. |
| PD | Predicate disambiguation | batch_69f75eb3d6f081908c933474eb359e3d |
completed | May 3, 2026, 2:41 p.m. |
Created at: April 29, 2026, 8:58 p.m.