Triple
T22181813
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | K9 Thunder export configuration for Norway |
E548186
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
K9NO
K9NO is the Norwegian variant of the South Korean K9 Thunder self-propelled howitzer, customized to meet Norway’s operational and technical requirements.
|
E1523318
|
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: K9NO | Statement: [K9 Thunder export configuration for Norway, alsoKnownAs, K9NO]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: K9NO Context triple: [K9 Thunder export configuration for Norway, alsoKnownAs, K9NO]
-
A.
K9K
K9K is a Canadian postal code prefix assigned to part of the city of Peterborough in Ontario.
-
B.
K-99
K-99 is a north–south state highway running through eastern Kansas, connecting several small towns and rural areas.
-
C.
K-9
K-9 is a 1989 American buddy cop comedy film starring James Belushi as a detective partnered with a police dog to take down a drug dealer.
-
D.
K-9
K-9 is a robotic dog from the Doctor Who universe, known as a loyal, intelligent companion equipped with advanced technology and weaponry.
-
E.
K9A1
K9A1 is an upgraded South Korean K9 Thunder self-propelled howitzer variant featuring improved fire control, automation, and crew ergonomics.
- 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: K9NO Triple: [K9 Thunder export configuration for Norway, alsoKnownAs, K9NO]
Generated description
K9NO is the Norwegian variant of the South Korean K9 Thunder self-propelled howitzer, customized to meet Norway’s operational and technical requirements.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: K9NO Target entity description: K9NO is the Norwegian variant of the South Korean K9 Thunder self-propelled howitzer, customized to meet Norway’s operational and technical requirements.
-
A.
K9K
K9K is a Canadian postal code prefix assigned to part of the city of Peterborough in Ontario.
-
B.
K-99
K-99 is a north–south state highway running through eastern Kansas, connecting several small towns and rural areas.
-
C.
K-9
K-9 is a 1989 American buddy cop comedy film starring James Belushi as a detective partnered with a police dog to take down a drug dealer.
-
D.
K-9
K-9 is a robotic dog from the Doctor Who universe, known as a loyal, intelligent companion equipped with advanced technology and weaponry.
-
E.
K9A1
K9A1 is an upgraded South Korean K9 Thunder self-propelled howitzer variant featuring improved fire control, automation, and crew ergonomics.
- 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_69e11e3d53f88190a2b690e3f25bb062 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12aa5998081909ff35f8b5df4f92f |
completed | April 28, 2026, 9:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a9ecd0a7881909dbda649b8474ff1 |
completed | May 18, 2026, 5:08 a.m. |
| NEDg | Description generation | batch_6a0a9f494f3c8190aee8f2e6dc1af763 |
completed | May 18, 2026, 5:10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0aa03a8e0c819089dc48473b5b743c |
completed | May 18, 2026, 5:14 a.m. |
Created at: April 16, 2026, 8:35 p.m.