Triple
T12949944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ramstein airshow disaster |
E309865
|
entity |
| Predicate | deadliestAirshowAccidentRank |
P107656
|
FINISHED |
| Object | one of the deadliest in 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: one of the deadliest in history | Statement: [Ramstein airshow disaster, deadliestAirshowAccidentRank, one of the deadliest in history]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: deadliestAirshowAccidentRank Context triple: [Ramstein airshow disaster, deadliestAirshowAccidentRank, one of the deadliest in history]
-
A.
aircraftAccidentYear
Indicates the calendar year in which an aircraft accident occurred.
-
B.
aircraftInvolvedInDeath
Indicates that an aircraft played a direct role in causing or contributing to a person's death.
-
C.
numberOfFatalAccidents
Indicates the total count of accidents within a given context that resulted in at least one fatality.
-
D.
flightNumberInAccident
Indicates that a specific flight number is associated with an accident event.
-
E.
missionAccident
Indicates that an accident or unintended harmful event occurred during the course of a mission or operation.
- 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_69d7bdfb57a88190836b743e2825feca |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e59a4c88190907d05b8d57dae89 |
completed | April 10, 2026, 10:48 p.m. |
| PD | Predicate disambiguation | batch_69d97dba57988190b786ffed55687a72 |
completed | April 10, 2026, 10:46 p.m. |
| PDg | Predicate description generation | batch_69d97e5811f481908178fac6d2e0efcd |
completed | April 10, 2026, 10:48 p.m. |
Created at: April 9, 2026, 5:43 p.m.