Triple
T2778791
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | QF |
E61640
|
entity |
| Predicate | airlineSafetyReputation |
P43128
|
FINISHED |
| Object | major international airline |
—
|
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: major international airline | Statement: [QF, airlineSafetyReputation, major international airline]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airlineSafetyReputation Context triple: [QF, airlineSafetyReputation, major international airline]
-
A.
airlineScale
Indicates that one airline is a subsidiary, regional brand, or otherwise operates at a smaller or supporting scale relative to another airline.
-
B.
airline
Indicates that an entity operates as a commercial air transport carrier providing flight services between locations.
-
C.
airlinesUse
Indicates that certain airlines operate, employ, or make use of a specified resource, service, or system.
-
D.
airlineCategory
Indicates the classification or type of an airline within a defined categorization system (e.g., full-service, low-cost, regional).
-
E.
airlineType
Indicates the classification or category of an airline based on its operational or service characteristics.
- F. None of above. chosen
Provenance (4 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_69ab4b7e43c48190997b8fc8fb1663ab |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abddceb9d88190961e30d521a21552 |
completed | March 7, 2026, 8:11 a.m. |
| PD | Predicate disambiguation | batch_69abdd00b65c8190a8ea444308c4fa2b |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abddcc348081908b5f760899389d4f |
completed | March 7, 2026, 8:11 a.m. |
Created at: March 6, 2026, 9:57 p.m.