Triple
T38020473
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wellington Wells |
E948614
|
entity |
| Predicate | drug |
P189865
|
FINISHED |
| Object | Joy |
—
|
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: Joy | Statement: [Wellington Wells, drug, Joy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: drug Context triple: [Wellington Wells, drug, Joy]
-
A.
primaryDrug
Indicates that one drug is identified as the main or most important medication in a given treatment, context, or combination relative to other associated drugs.
-
B.
drugPolicy
Indicates the rules, regulations, or guidelines governing the use, control, or management of drugs within a given context.
-
C.
usedSubstance
Indicates that an entity has consumed, applied, or otherwise made use of a particular substance.
-
D.
drugResponse
Indicates how an entity’s condition, behavior, or measurable outcome changes as a result of exposure to a particular drug.
-
E.
drugClass
Indicates that one entity is classified as a particular pharmacological or therapeutic category of drugs in relation to another entity.
- 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_69f76efc10448190aff5fb566b98f952 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fbca6c066c8190a1599202f341417f |
completed | May 6, 2026, 11:10 p.m. |
| PD | Predicate disambiguation | batch_69fbc8ee04f08190977b7ad70fc85896 |
completed | May 6, 2026, 11:04 p.m. |
| PDg | Predicate description generation | batch_69fbc993caa881908c16c3e21efaeef9 |
completed | May 6, 2026, 11:07 p.m. |
Created at: May 3, 2026, 4:20 p.m.