Triple
T5197553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | flydubai |
E117308
|
entity |
| Predicate | safetyRecordEvent |
P30019
|
FINISHED |
| Object | Flydubai Flight 981 crash in Rostov-on-Don in 2016 |
—
|
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: Flydubai Flight 981 crash in Rostov-on-Don in 2016 | Statement: [flydubai, safetyRecordEvent, Flydubai Flight 981 crash in Rostov-on-Don in 2016]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyRecordEvent Context triple: [flydubai, safetyRecordEvent, Flydubai Flight 981 crash in Rostov-on-Don in 2016]
-
A.
safetyOutcome
Indicates the resulting condition or consequence related to safety that arises from an action, event, or situation.
-
B.
hasSecurityEvent
chosen
Indicates that a security-related incident or event is associated with, or has occurred for, a given entity.
-
C.
notableSafety
Indicates that an entity is recognized for having significant safety characteristics, performance, or impact relative to others.
-
D.
safetyRelevant
Indicates that the associated entity, condition, or information has a direct impact on safety or is critical for preventing harm or accidents.
-
E.
safetyGoal
Indicates that an entity is associated with a specific safety objective or target condition intended to prevent harm or reduce risk.
- 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_69bd4462ed04819084fcb01eb9d2fa74 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7adb034c819086bf8a85fbf158f4 |
completed | March 20, 2026, 4:50 p.m. |
| PD | Predicate disambiguation | batch_69bd77b9a67c8190819612257ea746b4 |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:46 p.m.