Triple
T768558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Silver |
E16227
|
entity |
| Predicate | healthEffect |
P19730
|
FINISHED |
| Object | can cause argyria in excessive exposure |
—
|
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: can cause argyria in excessive exposure | Statement: [Silver, healthEffect, can cause argyria in excessive exposure]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: healthEffect Context triple: [Silver, healthEffect, can cause argyria in excessive exposure]
-
A.
involvedPhysicalEffect
Indicates that one entity participates in causing, experiencing, or mediating a physical effect on another entity or the environment.
-
B.
primaryEffect
Indicates the main direct outcome or consequence that results from a given cause, action, or condition.
-
C.
healthProxy
Indicates that one entity is authorized to make health-related or medical decisions on behalf of another entity.
-
D.
treats
Indicates that one entity provides medical care or therapeutic intervention to another entity.
-
E.
remedy
Indicates that one entity serves to cure, alleviate, or counteract a problem, illness, or undesirable condition affecting 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_69a49369a0848190af883934cee3db4c |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a765ba688190ab328bb159583077 |
completed | March 1, 2026, 8:53 p.m. |
| PD | Predicate disambiguation | batch_69a4a5074c788190a74fc20ad24e2d26 |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a7648a6c8190a9051a3d177ff7e2 |
completed | March 1, 2026, 8:53 p.m. |
Created at: March 1, 2026, 7:37 p.m.