Triple
T23343036
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2014 Mount Everest ice avalanche |
E591783
|
entity |
| Predicate | deadliestFor |
P151945
|
FINISHED |
| Object | Nepalese guides |
—
|
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: Nepalese guides | Statement: [2014 Mount Everest ice avalanche, deadliestFor, Nepalese guides]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: deadliestFor Context triple: [2014 Mount Everest ice avalanche, deadliestFor, Nepalese guides]
-
A.
deadliestIn
Indicates that something has the highest lethality or causes the most deaths within a specified context, group, or location.
-
B.
wasDeadly
Indicates that an event, action, or condition resulted in death or had the capacity to cause death.
-
C.
deathToll
Indicates the number of deaths resulting from a particular event, situation, or cause.
-
D.
fatalitiesCategory
Indicates the classification of deaths associated with an event, incident, or condition into a specific category or severity level.
-
E.
causedFatalities
Indicates that the referenced event or action directly resulted in one or more deaths.
- 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_69e25d20e3d08190bcede87673cafb25 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1983431f08190b0078728d44872a8 |
completed | April 29, 2026, 5:33 a.m. |
| PD | Predicate disambiguation | batch_69effcfd8d288190937a887fe6023c11 |
completed | April 28, 2026, 12:19 a.m. |
| PDg | Predicate description generation | batch_69f01d88b4ec8190a2a17a88e0eda178 |
completed | April 28, 2026, 2:38 a.m. |
Created at: April 17, 2026, 5:18 p.m.