Triple
T6004878
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SOD1 |
E133684
|
entity |
| Predicate | hasMutationEffect |
P22587
|
FINISHED |
| Object | gain-of-function toxicity in motor neurons |
—
|
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: gain-of-function toxicity in motor neurons | Statement: [SOD1, hasMutationEffect, gain-of-function toxicity in motor neurons]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMutationEffect Context triple: [SOD1, hasMutationEffect, gain-of-function toxicity in motor neurons]
-
A.
hasMutationSystem
Indicates that one entity possesses or employs a particular mutation system or mechanism for generating or managing mutations.
-
B.
hasCommonSideEffect
Indicates that two or more treatments, drugs, or interventions share at least one side effect in common.
-
C.
hasEffectNamedAfter
Indicates that an entity has an effect or phenomenon that is named after another entity.
-
D.
hasHumanModification
Indicates that an entity has been altered, influenced, or modified as a result of human activity or intervention.
-
E.
hasResultingChange
chosen
Indicates that one entity causes or leads to a specific change or transformation in another entity or state.
- 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_69c00872444c8190bfaf1739dcec765c |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04f10d18081908c351170b7f58d3d |
completed | March 22, 2026, 8:20 p.m. |
| PD | Predicate disambiguation | batch_69c049e3316c819087ea635fa7ee8472 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:06 p.m.