Triple
T25255033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pegasus Airlines Flight 8622 |
E633143
|
entity |
| Predicate | incidentCategory |
P76024
|
FINISHED |
| Object | runway excursion without fatalities |
—
|
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: runway excursion without fatalities | Statement: [Pegasus Airlines Flight 8622, incidentCategory, runway excursion without fatalities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: incidentCategory Context triple: [Pegasus Airlines Flight 8622, incidentCategory, runway excursion without fatalities]
-
A.
incidentWith
Indicates that one entity is involved in, affected by, or associated with a particular incident or event together with another entity.
-
B.
commonIncidentType
Indicates that multiple entities share the same category or type of incident.
-
C.
incidentName
Indicates the specific name or label assigned to an incident within a system or context.
-
D.
notableIncidentType
chosen
Indicates the specific category or kind of significant event or incident associated with an entity.
-
E.
incidentCharacterization
Indicates how an incident is classified or characterized in terms of its nature, attributes, or type.
- 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_69e75a922ad481908f4f1f884583cb42 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f4808f350881908ebf53f883f5e5b0 |
completed | May 1, 2026, 10:29 a.m. |
| PD | Predicate disambiguation | batch_69f44d849e7c81909945438f40e35362 |
completed | May 1, 2026, 6:51 a.m. |
Created at: April 21, 2026, 1:13 p.m.