Triple
T9129479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Special Warfare Education Group |
E219045
|
entity |
| Predicate | alternativeName |
P39
|
FINISHED |
| Object |
SWEG
SWEG is an acronym for the Special Warfare Education Group, a military organization focused on training and educating special operations personnel.
|
E779368
|
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: SWEG | Statement: [Special Warfare Education Group, alternativeName, SWEG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SWEG Context triple: [Special Warfare Education Group, alternativeName, SWEG]
-
A.
Suter
Suter is a surname of Germanic origin, often associated with individuals of Swiss or German heritage.
-
B.
Scania
Scania is a Swedish manufacturer renowned for its heavy trucks, buses, and industrial and marine engines.
-
C.
Scania
Scania is a historical province in southern Sweden known for its fertile farmland, coastal landscapes, and former status as part of Denmark.
-
D.
SKW
SKW is Poland’s Military Counterintelligence Service, responsible for protecting the armed forces and state defense structures from espionage, terrorism, and other security threats.
-
E.
SKW
SKW is the ICAO airline designator used to identify SkyWest Airlines in aviation operations and communications.
- 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: SWEG Triple: [Special Warfare Education Group, alternativeName, SWEG]
Generated description
SWEG is an acronym for the Special Warfare Education Group, a military organization focused on training and educating special operations personnel.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SWEG Target entity description: SWEG is an acronym for the Special Warfare Education Group, a military organization focused on training and educating special operations personnel.
-
A.
Suter
Suter is a surname of Germanic origin, often associated with individuals of Swiss or German heritage.
-
B.
Scania
Scania is a historical province in southern Sweden known for its fertile farmland, coastal landscapes, and former status as part of Denmark.
-
C.
Scania
Scania is a Swedish manufacturer renowned for its heavy trucks, buses, and industrial and marine engines.
-
D.
SKW
SKW is Poland’s Military Counterintelligence Service, responsible for protecting the armed forces and state defense structures from espionage, terrorism, and other security threats.
-
E.
SKW
SKW is the ICAO airline designator used to identify SkyWest Airlines in aviation operations and communications.
- 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_69ca83debfc0819095800583e97ab10f |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca8cdafb48190be5e62b15779d771 |
completed | April 1, 2026, 5:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d030a934988190976aa359f9f902c3 |
completed | April 3, 2026, 9:27 p.m. |
| NEDg | Description generation | batch_69d032a7d9808190a4b3be86e49afb22 |
completed | April 3, 2026, 9:35 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d0337057048190af062152688599a1 |
completed | April 3, 2026, 9:38 p.m. |
Created at: March 30, 2026, 7:18 p.m.