Triple
T9594949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SK-105 Kürassier |
E231507
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object |
SK-105A1
The SK-105A1 is an upgraded variant of the Austrian SK-105 Kürassier light tank destroyer, featuring improved fire control and combat capabilities.
|
E808553
|
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: SK-105A1 | Statement: [SK-105 Kürassier, hasVariant, SK-105A1]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SK-105A1 Context triple: [SK-105 Kürassier, hasVariant, SK-105A1]
-
A.
A5103
A5103 is a primary A-road in Manchester, England, providing a key route between the city centre and the M56 motorway.
-
B.
S-101
S-101 is the International Hydrographic Organization’s modern electronic navigational chart standard designed to support next-generation marine navigation systems.
-
C.
A-Spec
A-Spec is Acura’s sport-oriented trim and styling package that enhances select models with more aggressive design cues and performance-focused features.
-
D.
SKB
SKB is the German Bundeswehr’s Joint Support Service, responsible for providing cross-branch logistical, administrative, and operational support to the armed forces.
-
E.
A51
A51 is the station code for the Euclid Avenue subway station in New York City’s transit system.
- 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: SK-105A1 Triple: [SK-105 Kürassier, hasVariant, SK-105A1]
Generated description
The SK-105A1 is an upgraded variant of the Austrian SK-105 Kürassier light tank destroyer, featuring improved fire control and combat capabilities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SK-105A1 Target entity description: The SK-105A1 is an upgraded variant of the Austrian SK-105 Kürassier light tank destroyer, featuring improved fire control and combat capabilities.
-
A.
A5103
A5103 is a primary A-road in Manchester, England, providing a key route between the city centre and the M56 motorway.
-
B.
S-101
S-101 is the International Hydrographic Organization’s modern electronic navigational chart standard designed to support next-generation marine navigation systems.
-
C.
A-Spec
A-Spec is Acura’s sport-oriented trim and styling package that enhances select models with more aggressive design cues and performance-focused features.
-
D.
SKB
SKB is the German Bundeswehr’s Joint Support Service, responsible for providing cross-branch logistical, administrative, and operational support to the armed forces.
-
E.
A51
A51 is the station code for the Euclid Avenue subway station in New York City’s transit system.
- 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_69ca8482884481908eccdfdf64d6fbf7 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9a14fd308190beb8fda5a9e912c7 |
completed | April 1, 2026, 10:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d16199845881908f8a91182ca48250 |
completed | April 4, 2026, 7:08 p.m. |
| NEDg | Description generation | batch_69d16230a99481909d82d03babe6729a |
completed | April 4, 2026, 7:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d16321aba88190a7e8359dbaa5362b |
completed | April 4, 2026, 7:14 p.m. |
Created at: March 30, 2026, 8:07 p.m.