Triple
T10413461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EuroBonus |
E245453
|
entity |
| Predicate | hasDigitalChannel |
P10738
|
FINISHED |
| Object |
SAS mobile app
The SAS mobile app is Scandinavian Airlines’ official smartphone application that lets users manage flights, check in, access boarding passes, and handle EuroBonus loyalty services on the go.
|
E862148
|
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: SAS mobile app | Statement: [EuroBonus, hasDigitalChannel, SAS mobile app]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SAS mobile app Context triple: [EuroBonus, hasDigitalChannel, SAS mobile app]
-
A.
SAS
SAS is a widely used statistical software suite for advanced analytics, business intelligence, data management, and predictive modeling.
-
B.
SAS
SAS is a high-speed, point-to-point serial interface standard commonly used to connect enterprise storage devices like hard drives and solid-state drives to servers.
-
C.
SAS
SAS is the standard abbreviation used for the NBA team San Antonio Spurs.
-
D.
SAS
SAS is the station code for San Antonio railway station.
-
E.
SAS
SAS is the School of Arts and Sciences at the University of Pennsylvania, encompassing the university’s core liberal arts and sciences departments and programs.
- 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: SAS mobile app Triple: [EuroBonus, hasDigitalChannel, SAS mobile app]
Generated description
The SAS mobile app is Scandinavian Airlines’ official smartphone application that lets users manage flights, check in, access boarding passes, and handle EuroBonus loyalty services on the go.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SAS mobile app Target entity description: The SAS mobile app is Scandinavian Airlines’ official smartphone application that lets users manage flights, check in, access boarding passes, and handle EuroBonus loyalty services on the go.
-
A.
SAS
SAS is a widely used statistical software suite for advanced analytics, business intelligence, data management, and predictive modeling.
-
B.
SAS
SAS is a high-speed, point-to-point serial interface standard commonly used to connect enterprise storage devices like hard drives and solid-state drives to servers.
-
C.
SAS
SAS is the standard abbreviation used for the NBA team San Antonio Spurs.
-
D.
SAS
SAS is the station code for San Antonio railway station.
-
E.
SAS
SAS is the School of Arts and Sciences at the University of Pennsylvania, encompassing the university’s core liberal arts and sciences departments and programs.
- 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_69d381be340c8190b05998703d42d224 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4ea0ec6fc8190a71af759226a3cba |
completed | April 7, 2026, 11:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d7fc043074819083d219ec1c40f931 |
completed | April 9, 2026, 7:20 p.m. |
| NEDg | Description generation | batch_69d822d6a0188190a7ee6de5aab50486 |
completed | April 9, 2026, 10:06 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d859e40bf88190a6dc8deed3049d31 |
completed | April 10, 2026, 2:01 a.m. |
Created at: April 6, 2026, 12:10 p.m.