Triple
T19017252
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2016 Fort McMurray wildfire |
E465383
|
entity |
| Predicate | indirectFatalitiesCause |
P134135
|
FINISHED |
| Object | motor vehicle collision during evacuation |
—
|
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: motor vehicle collision during evacuation | Statement: [2016 Fort McMurray wildfire, indirectFatalitiesCause, motor vehicle collision during evacuation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: indirectFatalitiesCause Context triple: [2016 Fort McMurray wildfire, indirectFatalitiesCause, motor vehicle collision during evacuation]
-
A.
causedFatalities
Indicates that the referenced event or action directly resulted in one or more deaths.
-
B.
fatalitiesCategory
Indicates the classification of deaths associated with an event, incident, or condition into a specific category or severity level.
-
C.
causeOfDeath
Indicates the specific factor, event, or condition that directly resulted in an entity’s death.
-
D.
primaryCasualtiesFrom
Indicates that an entity is the main source or cause of the casualties experienced by another entity.
-
E.
deathToll
Indicates the number of deaths resulting from a particular event, situation, or cause.
- 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_69d8dd025c188190a1d81f5b4ec7e2c6 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d6dc0f9081909cddc5186603e885 |
completed | April 20, 2026, 7:33 a.m. |
| PD | Predicate disambiguation | batch_69e4a2fd80c081908237317a3a883e1c |
completed | April 19, 2026, 9:40 a.m. |
| PDg | Predicate description generation | batch_69e4ad8e075c8190ad561edc5e520057 |
completed | April 19, 2026, 10:25 a.m. |
Created at: April 10, 2026, 12:02 p.m.