Triple
T36618804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | epidermal growth factor receptor family |
E903677
|
entity |
| Predicate | encodesProteinType |
P66851
|
FINISHED |
| Object | receptor tyrosine kinase |
—
|
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: receptor tyrosine kinase | Statement: [epidermal growth factor receptor family, encodesProteinType, receptor tyrosine kinase]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: encodesProteinType Context triple: [epidermal growth factor receptor family, encodesProteinType, receptor tyrosine kinase]
-
A.
encodedByGene
Indicates that a particular gene is responsible for producing or specifying the sequence of a given molecule (such as a protein or RNA).
-
B.
geneType
Indicates the specific category or classification of a gene based on its functional or structural characteristics.
-
C.
encodes
Indicates that one entity contains or represents the information, instructions, or structure of another in a coded or symbolic form.
-
D.
typicalProtein
chosen
Indicates that one entity is a representative or characteristic example of a particular protein type or class.
-
E.
ribosomeType
Indicates the specific kind or category of ribosome associated with or used by an 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_69f76e6960e4819092047756ceb9a17e |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7c777e924819081a6634f549fe552 |
completed | May 3, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69f7c477a4d481908f52e55b6688f60c |
completed | May 3, 2026, 9:56 p.m. |
Created at: May 3, 2026, 4:11 p.m.