Triple
T34821667
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greensburg, Kansas |
E1003793
|
entity |
| Predicate | tornadoDamage |
P54661
|
FINISHED |
| Object | about 95 percent of the town destroyed |
—
|
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 95 percent of the town destroyed | Statement: [Greensburg, Kansas, tornadoDamage, about 95 percent of the town destroyed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tornadoDamage Context triple: [Greensburg, Kansas, tornadoDamage, about 95 percent of the town destroyed]
-
A.
majorTornadoEvent
Indicates that a tornado event is of significant intensity or impact, typically meeting defined thresholds for severity or damage.
-
B.
regionAffectedByNamedStorms
Indicates that a geographic region is impacted or influenced by one or more specifically named storms.
-
C.
infrastructureDamage
chosen
Indicates damage or destruction affecting physical infrastructure such as buildings, roads, utilities, or other constructed facilities.
-
D.
tornado2011Description
Indicates that the entity provides a textual description or summary of the 2011 tornado event.
-
E.
tornadoDamage1997
Indicates that an entity experienced damage caused by a tornado in the year 1997.
- 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_69f78c61ed4c8190ad84c918fa9af55a |
completed | May 3, 2026, 5:56 p.m. |
| PD | Predicate disambiguation | batch_69f78b8cb3a881909ebaac1b503988c2 |
completed | May 3, 2026, 5:53 p.m. |
Created at: May 3, 2026, 4 p.m.