Triple
T19245330
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | INS gene |
E481234
|
entity |
| Predicate | proteinProduct |
P135043
|
FINISHED |
| Object | insulin precursor |
—
|
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: insulin precursor | Statement: [INS gene, proteinProduct, insulin precursor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: proteinProduct Context triple: [INS gene, proteinProduct, insulin precursor]
-
A.
proteinContent
Indicates the amount or proportion of protein present in a given entity or substance.
-
B.
uniprotId
Indicates that an entity is associated with a specific UniProt database identifier for a protein.
-
C.
byProduct
Indicates that one entity is produced incidentally or as a secondary result of a process, activity, or creation involving another entity.
-
D.
productFor
Indicates that one entity is intended to be used by, with, or in relation to another entity as its product or offering.
-
E.
typicalProtein
Indicates that one entity is a representative or characteristic example of a particular protein type or class.
- 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_69d8e8cd9d1081908a181d02b88b59b8 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fb2c34e88190a338e5a7ba906425 |
completed | April 20, 2026, 10:08 a.m. |
| PD | Predicate disambiguation | batch_69e4dd002d00819088b625056edfb74e |
completed | April 19, 2026, 1:47 p.m. |
| PDg | Predicate description generation | batch_69e4ddcf50108190a09d0f1291c17374 |
completed | April 19, 2026, 1:51 p.m. |
Created at: April 10, 2026, 1:27 p.m.