Triple
T4220288
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Air France Flight 4590 |
E94320
|
entity |
| Predicate | impactOnAviation |
P49069
|
FINISHED |
| Object | led to safety modifications for Concorde fuel tanks |
—
|
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: led to safety modifications for Concorde fuel tanks | Statement: [Air France Flight 4590, impactOnAviation, led to safety modifications for Concorde fuel tanks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impactOnAviation Context triple: [Air France Flight 4590, impactOnAviation, led to safety modifications for Concorde fuel tanks]
-
A.
aircraftImpact
Indicates that an aircraft collides with or crashes into a target or surface, causing an impact event.
-
B.
affectsAirTraffic
chosen
Indicates that one entity causes changes or disruptions to the normal flow, safety, or management of air traffic.
-
C.
aviationUsage
Indicates that something is used for, involved in, or associated with aviation-related activities or purposes.
-
D.
usedInAviation
Indicates that something is employed or applied within the field or context of aviation.
-
E.
aircraft
Indicates that an entity is an aircraft or functions in the role of an aircraft in the described context.
- 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_69b3451997e08190851db4a9a588837d |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b34e4bf6088190926b982039a12079 |
completed | March 12, 2026, 11:37 p.m. |
| PD | Predicate disambiguation | batch_69b347f1d7b48190bd8974c03c7dc937 |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:04 p.m.