Triple
T4945907
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | insulin |
E111048
|
entity |
| Predicate | usedToTreat |
P4714
|
FINISHED |
| Object | type 1 diabetes mellitus |
—
|
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: type 1 diabetes mellitus | Statement: [insulin, usedToTreat, type 1 diabetes mellitus]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedToTreat Context triple: [insulin, usedToTreat, type 1 diabetes mellitus]
-
A.
treats
Indicates that one entity provides medical care or therapeutic intervention to another entity.
-
B.
hasCommonTreatment
Indicates that two or more entities share at least one treatment method or therapeutic approach in common.
-
C.
usedToBe
Indicates that something held a particular state, role, or property in the past but no longer does in the present.
-
D.
treatment
chosen
Indicates that one entity is used as a medical or therapeutic intervention to address, manage, or cure a condition affecting another entity.
-
E.
usesDrug
Indicates that an entity consumes, administers, or otherwise makes use of a specified drug.
- 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_69bd441721cc819085c7e33fe0876818 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd70aa890c81908e685ec5e88cae1f |
completed | March 20, 2026, 4:07 p.m. |
| PD | Predicate disambiguation | batch_69bd6c3aa1388190b3e0c8ee1ba1e4fa |
completed | March 20, 2026, 3:48 p.m. |
Created at: March 20, 2026, 1:31 p.m.