Triple
T32551243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Johnstown Flood of 1889 |
E831977
|
entity |
| Predicate | oneOfDeadliest |
P30647
|
FINISHED |
| Object | disasters in U.S. history |
—
|
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: disasters in U.S. history | Statement: [Great Johnstown Flood of 1889, oneOfDeadliest, disasters in U.S. history]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oneOfDeadliest Context triple: [Great Johnstown Flood of 1889, oneOfDeadliest, disasters in U.S. history]
-
A.
deadliestIn
chosen
Indicates that something has the highest lethality or causes the most deaths within a specified context, group, or location.
-
B.
deadliestFor
Indicates that one entity causes the greatest number of deaths or is most lethal specifically with respect to another entity or group.
-
C.
deathToll
Indicates the number of deaths resulting from a particular event, situation, or cause.
-
D.
oneOfWorstDisastersIn
Indicates that an event is among the most severe or catastrophic disasters that have occurred within a specified place or region.
-
E.
causedFatalities
Indicates that the referenced event or action directly resulted in one or more 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_69f34925fd08819084cfe4ec566cb704 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6cd126fcc8190aa1f1f146e45ec0c |
completed | May 3, 2026, 4:20 a.m. |
| PD | Predicate disambiguation | batch_69f6cc1470808190b70cdfd7a6395670 |
completed | May 3, 2026, 4:16 a.m. |
Created at: May 1, 2026, 1:02 a.m.