Triple
T3833541
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VX |
E91072
|
entity |
| Predicate | LD50DermalHumanEstimate |
P52268
|
FINISHED |
| Object | about 10 mg |
—
|
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: about 10 mg | Statement: [VX, LD50DermalHumanEstimate, about 10 mg]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: LD50DermalHumanEstimate Context triple: [VX, LD50DermalHumanEstimate, about 10 mg]
-
A.
toxinType
Indicates the specific kind or category of toxin associated with an entity.
-
B.
toxicTo
Indicates that one entity causes harm, poisoning, or adverse effects to another when exposed or applied.
-
C.
zoonoticPotential
Indicates the potential for a disease or pathogen to be transmitted from animals to humans.
-
D.
mortalityRate
Indicates the proportion of individuals in a defined population that die within a specified time period.
-
E.
skinThickness
Indicates the measured thickness of an entity’s skin, typically quantifying how thick its outer tissue layer is.
- 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_69aed960b538819096561c8ed448dec9 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeeb88b8a8819082d4bdbc5bc45366 |
completed | March 9, 2026, 3:47 p.m. |
| PD | Predicate disambiguation | batch_69aee74c2e04819094b94b3c0bac1806 |
completed | March 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69aeeb828fb08190901d51edbe8bd304 |
completed | March 9, 2026, 3:47 p.m. |
Created at: March 9, 2026, 3:17 p.m.