Triple
T38561033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | tetanus |
E928075
|
entity |
| Predicate | hasHighMortalityWithoutTreatment |
P201177
|
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: [tetanus, hasHighMortalityWithoutTreatment, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHighMortalityWithoutTreatment Context triple: [tetanus, hasHighMortalityWithoutTreatment, true]
-
A.
mortalityRate
Indicates the proportion of individuals in a defined population that die within a specified time period.
-
B.
hasHighChanceOfSurvival
Indicates that the subject is very likely to continue living or remain viable under the given conditions.
-
C.
deathWithoutIssue
Indicates that a person has died without leaving any surviving descendants or heirs.
-
D.
hasNoApprovedDrugTreatment
Indicates that there is currently no officially approved drug-based treatment available for the condition or situation in question.
-
E.
doesNotCure
Indicates that an action, treatment, or intervention fails to eliminate or resolve a condition, problem, or disease in the affected 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_69f76eb8d1808190a588af29d8b266d6 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69ffcf46fd688190907fd1ceb499a8d1 |
completed | May 10, 2026, 12:20 a.m. |
| PD | Predicate disambiguation | batch_69ffccde2a8c81908e055e74077dbd19 |
completed | May 10, 2026, 12:10 a.m. |
| PDg | Predicate description generation | batch_69ffcf4631cc8190aefa4b8f0b940b89 |
completed | May 10, 2026, 12:20 a.m. |
Created at: May 3, 2026, 4:32 p.m.