Triple
T36013941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elliot McKay See Jr. |
E1041786
|
entity |
| Predicate | aircraftTypeInFatalAccident |
P1524
|
FINISHED |
| Object | T-38 Talon |
—
|
NE NERFINISHED |
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: T-38 Talon | Statement: [Elliot McKay See Jr., aircraftTypeInFatalAccident, T-38 Talon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: aircraftTypeInFatalAccident Context triple: [Elliot McKay See Jr., aircraftTypeInFatalAccident, T-38 Talon]
-
A.
aircraftType
chosen
Indicates the specific model or category of aircraft associated with an entity or event.
-
B.
aircraftInvolvedInDeath
Indicates that an aircraft played a direct role in causing or contributing to a person's death.
-
C.
aircraftAccident
Indicates that an event involves an aircraft experiencing an accident, such as a crash, collision, or serious malfunction during operation.
-
D.
accidentInvolvedAircraft
Indicates that an aircraft was directly involved in an accident or incident.
-
E.
typeOfAviation
Indicates the specific category or kind of aviation to which an entity belongs (e.g., commercial, military, private).
- 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_69f76e2b981881908e4e160607fa82eb |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7b2c771108190adeec151daad5dab |
completed | May 3, 2026, 8:40 p.m. |
| PD | Predicate disambiguation | batch_69f7b1bad2e88190963ab4ee5d4f2038 |
completed | May 3, 2026, 8:36 p.m. |
Created at: May 3, 2026, 4:07 p.m.