Triple
T14271883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hendra virus |
E353805
|
entity |
| Predicate | caseFatalityRateInHorses |
P20197
|
FINISHED |
| Object | high |
—
|
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: high | Statement: [Hendra virus, caseFatalityRateInHorses, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: caseFatalityRateInHorses Context triple: [Hendra virus, caseFatalityRateInHorses, high]
-
A.
caseFatalityRate
chosen
Indicates the proportion of deaths among all identified cases of a particular disease or condition within a specified period.
-
B.
mortalityRate
Indicates the proportion of individuals in a defined population that die within a specified time period.
-
C.
numberOfCattleKilled
Indicates the quantity of cattle that were killed in a given event or context.
-
D.
fatalitiesCategory
Indicates the classification of deaths associated with an event, incident, or condition into a specific category or severity level.
-
E.
casualtiesAnimal
Indicates that an animal has been harmed, injured, or killed as a result of a particular event or situation.
- 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_69d8278d25148190abf1a8c8f5f533ad |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de65811d7c8190b075909a6570d415 |
completed | April 14, 2026, 4:04 p.m. |
| PD | Predicate disambiguation | batch_69de2a7d586c8190846ff242bbf5ac53 |
completed | April 14, 2026, 11:52 a.m. |
Created at: April 10, 2026, 1:10 a.m.