Triple
T35419736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeffrey Skiles |
E1023747
|
entity |
| Predicate | numberOfFatalitiesOnNotableFlight |
P33471
|
FINISHED |
| Object | 0 |
—
|
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: 0 | Statement: [Jeffrey Skiles, numberOfFatalitiesOnNotableFlight, 0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFatalitiesOnNotableFlight Context triple: [Jeffrey Skiles, numberOfFatalitiesOnNotableFlight, 0]
-
A.
numberOfUSFatalities
Indicates the number of people who died in the United States as a result of the specified event or circumstance.
-
B.
aircraftInvolvedInDeath
Indicates that an aircraft played a direct role in causing or contributing to a person's death.
-
C.
numberOfDeathsInNotableEvent
Indicates the total count of deaths that occurred as a result of a specified notable event.
-
D.
aircraftAccident
Indicates that an event involves an aircraft experiencing an accident, such as a crash, collision, or serious malfunction during operation.
-
E.
fatalitiesOnboard
chosen
Indicates that the relationship specifies the number of people who died among those present on a particular vehicle or craft.
- 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_69f76df6704081909900c60be10d5849 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037c8d06cc8190ab6a5e18d9d2571e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0324d08190ac5b610cc0f6a38c |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:03 p.m.