Triple
T34821666
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greensburg, Kansas |
E1003793
|
entity |
| Predicate | tornadoFatalities |
P1785
|
FINISHED |
| Object | 11 people killed |
—
|
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: 11 people killed | Statement: [Greensburg, Kansas, tornadoFatalities, 11 people killed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tornadoFatalities Context triple: [Greensburg, Kansas, tornadoFatalities, 11 people killed]
-
A.
tornadoFatalities1997
Indicates the number of fatalities caused by tornadoes in the year 1997.
-
B.
majorTornadoEvent
Indicates that a tornado event is of significant intensity or impact, typically meeting defined thresholds for severity or damage.
-
C.
tornadoIntensityHistory
Indicates the recorded progression of a tornado’s intensity over time.
-
D.
deathToll
chosen
Indicates the number of deaths resulting from a particular event, situation, or cause.
-
E.
notableDeathTollEvent
Indicates that an event is characterized by causing an unusually large or historically significant number of deaths.
- 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_69f76db717088190811b4e744610f37d |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f7886be6d8819095ec62e4f2cee858 |
completed | May 3, 2026, 5:39 p.m. |
| PD | Predicate disambiguation | batch_69f7841440f48190b4346c08855951d2 |
completed | May 3, 2026, 5:21 p.m. |
Created at: May 3, 2026, 4 p.m.