Triple
T34148877
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marburg virus |
E875939
|
entity |
| Predicate | hasNoSpecificAntiviralTreatment |
P203970
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Marburg virus, hasNoSpecificAntiviralTreatment, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNoSpecificAntiviralTreatment Context triple: [Marburg virus, hasNoSpecificAntiviralTreatment, true]
-
A.
hasNoApprovedDrugTreatment
Indicates that there is currently no officially approved drug-based treatment available for the condition or situation in question.
-
B.
hasNoCure
Indicates that there is currently no known treatment capable of curing the referenced condition or problem.
-
C.
doesNotCure
Indicates that an action, treatment, or intervention fails to eliminate or resolve a condition, problem, or disease in the affected entity.
-
D.
hasHighMortalityWithoutTreatment
Indicates that, in the absence of appropriate treatment, the condition or situation is likely to result in a high rate of death among affected individuals.
-
E.
hasReceivedTreatmentFor
Indicates that an entity has undergone or been given a treatment in relation to a specified condition, issue, or problem.
- 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_69f349abaa508190a820f206620efddc |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a0301289dbc8190a4372958d3451171 |
completed | May 12, 2026, 10:30 a.m. |
| PD | Predicate disambiguation | batch_6a0300d4185c8190a383d5da3659bc4f |
completed | May 12, 2026, 10:28 a.m. |
| PDg | Predicate description generation | batch_6a030127f5ec8190add78cf709b57611 |
completed | May 12, 2026, 10:30 a.m. |
Created at: May 1, 2026, 1:54 a.m.