Triple
T33396040
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Air Astana |
E855172
|
entity |
| Predicate | safetyRecognition |
P54985
|
FINISHED |
| Object | Skytrax 4-star airline rating |
—
|
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: Skytrax 4-star airline rating | Statement: [Air Astana, safetyRecognition, Skytrax 4-star airline rating]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyRecognition Context triple: [Air Astana, safetyRecognition, Skytrax 4-star airline rating]
-
A.
safetyPrograms
Indicates that there are organized measures, policies, or initiatives implemented to protect people or assets from harm or risk.
-
B.
safetyRatingHighlight
Indicates that an entity’s safety rating is emphasized or specially marked as noteworthy compared to others.
-
C.
safetyBenefit
Indicates that one entity provides, contributes to, or results in an improvement in the safety or risk reduction experienced by another entity.
-
D.
safetyCategory
chosen
Indicates the classification of something according to its level or type of safety.
-
E.
safetyInnovationBy
Indicates that a safety-related innovation, measure, or improvement is created, introduced, or implemented by a specific agent or entity.
- 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_69f3496e3f1c8190bcecfa82aa9d17ff |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f70b966860819089cf92927f47c5f1 |
completed | May 3, 2026, 8:47 a.m. |
| PD | Predicate disambiguation | batch_69f70abe43e08190b2a30930d96247c1 |
completed | May 3, 2026, 8:43 a.m. |
Created at: May 1, 2026, 1:35 a.m.