Triple
T7501167
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tirana International Airport Nënë Tereza |
E177262
|
entity |
| Predicate | hubFor |
P423
|
FINISHED |
| Object |
Albawings
Albawings is an Albanian low-cost airline based in Tirana that operates short-haul passenger flights, primarily connecting Albania with destinations in Europe.
|
E668653
|
NE FINISHED |
How this triple was built (4 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: Albawings | Statement: [Tirana International Airport Nënë Tereza, hubFor, Albawings]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Albawings Context triple: [Tirana International Airport Nënë Tereza, hubFor, Albawings]
-
A.
Equair
Equair is an Ecuadorian airline that operated domestic passenger flights, notably serving routes from Guayaquil and Quito.
-
B.
ANA Wings
ANA Wings is a Japanese regional airline operating domestic feeder and short-haul services on behalf of All Nippon Airways.
-
C.
Wing
Wing is an experimental mobile operating system and user interface project developed by X (formerly Google X) to explore new paradigms in smartphone interaction and design.
-
D.
Wing
Wing is an Alphabet Inc. subsidiary focused on developing and operating drone-based delivery services and related logistics technologies.
-
E.
Canair
Canair was a Spanish regional airline that operated inter-island flights in the Canary Islands.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Albawings Triple: [Tirana International Airport Nënë Tereza, hubFor, Albawings]
Generated description
Albawings is an Albanian low-cost airline based in Tirana that operates short-haul passenger flights, primarily connecting Albania with destinations in Europe.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Albawings Target entity description: Albawings is an Albanian low-cost airline based in Tirana that operates short-haul passenger flights, primarily connecting Albania with destinations in Europe.
-
A.
Equair
Equair is an Ecuadorian airline that operated domestic passenger flights, notably serving routes from Guayaquil and Quito.
-
B.
ANA Wings
ANA Wings is a Japanese regional airline operating domestic feeder and short-haul services on behalf of All Nippon Airways.
-
C.
Wing
Wing is an experimental mobile operating system and user interface project developed by X (formerly Google X) to explore new paradigms in smartphone interaction and design.
-
D.
Wing
Wing is an Alphabet Inc. subsidiary focused on developing and operating drone-based delivery services and related logistics technologies.
-
E.
Canair
Canair was a Spanish regional airline that operated inter-island flights in the Canary Islands.
- F. None of above. chosen
Provenance (5 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_69c69f2696688190915a8458f2398211 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f59aabb8819085bdbe9c793d5b8b |
completed | March 27, 2026, 9:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c83c9490fc81908d35c0537b45aa13 |
completed | March 28, 2026, 8:39 p.m. |
| NEDg | Description generation | batch_69c83e17c25c8190a7a329f0a6169fac |
completed | March 28, 2026, 8:46 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c83ef9ce408190907a62c9d0a6dc16 |
completed | March 28, 2026, 8:50 p.m. |
Created at: March 27, 2026, 3:44 p.m.