Triple
T36599089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EGFR |
E902872
|
entity |
| Predicate | targetOfDrugClass |
P173861
|
FINISHED |
| Object | EGFR tyrosine kinase inhibitors |
—
|
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: EGFR tyrosine kinase inhibitors | Statement: [EGFR, targetOfDrugClass, EGFR tyrosine kinase inhibitors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetOfDrugClass Context triple: [EGFR, targetOfDrugClass, EGFR tyrosine kinase inhibitors]
-
A.
drugClass
Indicates that one entity is classified as a particular pharmacological or therapeutic category of drugs in relation to another entity.
-
B.
drugClassComponent
chosen
Indicates that one entity is a component, ingredient, or member of a specified drug class.
-
C.
hasPharmacologicClass
Indicates that a drug or medicinal product belongs to a specific pharmacologic class based on its mechanism of action or therapeutic effect.
-
D.
drugClassEvaluated
Indicates that the classification or category of a drug has been assessed or analyzed in a given context.
-
E.
isTherapeuticTargetIn
Indicates that a biological entity (e.g., gene, protein, pathway) is used or proposed as a therapeutic target within a specified disease, condition, or treatment context.
- 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_69f76e66b7b88190848f7a3e1188915f |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69ff16775a9881909d26dbc1f0ef3e1c |
completed | May 9, 2026, 11:11 a.m. |
| PD | Predicate disambiguation | batch_69ff158e61708190a1c581d0d306cfce |
completed | May 9, 2026, 11:07 a.m. |
Created at: May 3, 2026, 4:11 p.m.