Triple
T442834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cannabis |
E10150
|
entity |
| Predicate | medicalUseIncludes |
P8786
|
FINISHED |
| Object | chronic pain management |
—
|
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: chronic pain management | Statement: [Cannabis, medicalUseIncludes, chronic pain management]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: medicalUseIncludes Context triple: [Cannabis, medicalUseIncludes, chronic pain management]
-
A.
hasNotableDrug
Indicates that an entity is associated with a drug that is considered notable or significant in some recognized context.
-
B.
eligibleUses
Indicates the types of actions, purposes, or contexts in which something is permitted or qualified to be used.
-
C.
widelyUsedIn
Indicates that something is commonly or extensively utilized within a particular context, domain, or group.
-
D.
treats
chosen
Indicates that one entity provides medical care or therapeutic intervention to another entity.
-
E.
mayBeComorbidWith
Indicates that two conditions or disorders can occur together in the same individual, potentially influencing each other’s presence or severity.
- 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_69a2e8465ef481909655c681b01e2986 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2ef43e8f88190a5d368add11a38c0 |
completed | Feb. 28, 2026, 1:36 p.m. |
| PD | Predicate disambiguation | batch_69a2edde2b9c8190bd20b582eb4c5065 |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.