Triple
T9284548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EGF |
E223156
|
entity |
| Predicate | airlineTypeAssociated |
P73988
|
FINISHED |
| Object | regional 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: regional airline | Statement: [EGF, airlineTypeAssociated, regional airline]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airlineTypeAssociated Context triple: [EGF, airlineTypeAssociated, regional airline]
-
A.
airlineType
Indicates the classification or category of an airline based on its operational or service characteristics.
-
B.
associatedAirlineType
chosen
Indicates a relationship where an airline is linked to a specific classification or type (e.g., carrier category or operational class).
-
C.
airlineAssociatedBrand
Indicates that a brand is commercially or operationally associated with a particular airline, such as through co-branding, partnership, or subsidiary relationships.
-
D.
associatedAirlineServiceType
Indicates the specific type or category of airline service that is linked or related to another entity or activity.
-
E.
servesAirlineType
Indicates that a service provider (such as an airport, terminal, or facility) accommodates or operates flights for a specified type or category of airline.
- 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_69ca842123588190b3f2e1a69037d141 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd081e72988190917f425e64631837 |
completed | April 1, 2026, 11:57 a.m. |
| PD | Predicate disambiguation | batch_69cc7a576ec88190bbb787eb82e2e539 |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:34 p.m.