Triple
T3300578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Economy Class |
E69319
|
entity |
| Predicate | revenueImportanceForAirlines |
P47859
|
FINISHED |
| Object | high volume |
—
|
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: high volume | Statement: [Economy Class, revenueImportanceForAirlines, high volume]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: revenueImportanceForAirlines Context triple: [Economy Class, revenueImportanceForAirlines, high volume]
-
A.
mainAirlineFocus
Indicates that an airline is the primary or central focus of attention, operations, or analysis in a given context.
-
B.
airlinesUse
Indicates that certain airlines operate, employ, or make use of a specified resource, service, or system.
-
C.
airlineScale
Indicates that one airline is a subsidiary, regional brand, or otherwise operates at a smaller or supporting scale relative to another airline.
-
D.
tourismImportance
Indicates the degree to which a place or entity is significant or valuable as a destination or attraction for tourists.
-
E.
airlineSafetyReputation
Indicates the perceived level of safety and reliability associated with an airline’s operations and history.
- 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_69ad859e529c8190a404273f53cb487d |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb0a66fcc819093931fe7a6507723 |
completed | March 8, 2026, 5:23 p.m. |
| PD | Predicate disambiguation | batch_69ada42625308190be257f16a623a410 |
completed | March 8, 2026, 4:30 p.m. |
| PDg | Predicate description generation | batch_69ada526764881908e4bd52938d5374d |
completed | March 8, 2026, 4:34 p.m. |
Created at: March 8, 2026, 3:11 p.m.