Triple
T2989696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | US Airways Flight 1549 |
E80715
|
entity |
| Predicate | impactOnRegulation |
P40280
|
FINISHED |
| Object | prompted review of bird-strike and ditching procedures |
—
|
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: prompted review of bird-strike and ditching procedures | Statement: [US Airways Flight 1549, impactOnRegulation, prompted review of bird-strike and ditching procedures]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impactOnRegulation Context triple: [US Airways Flight 1549, impactOnRegulation, prompted review of bird-strike and ditching procedures]
-
A.
impactOnLaw
Indicates the effect or influence that one entity, event, or action has on laws, legal rules, or the legal system.
-
B.
safetyRegulationEffect
chosen
Indicates how a safety regulation influences or changes the conditions, behaviors, or outcomes associated with the regulated entities.
-
C.
legislativeImpact
Indicates the effect that a law or legislative action has on a policy, entity, or outcome.
-
D.
impactOnMarket
Indicates the effect or influence that one factor, event, or action has on market conditions, behavior, or outcomes.
-
E.
regulatoryIssues
Indicates that there are regulatory concerns, non-compliance, or potential violations associated with the related entity or activity.
- 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_69ad8b16c3488190b47b6aa7a59a335b |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad99dcdb00819092ca5f10396408e0 |
completed | March 8, 2026, 3:46 p.m. |
| PD | Predicate disambiguation | batch_69ad961403108190bbecb8d3608fd4e0 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:59 p.m.