Triple
T8066241
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | District of Mittelsachsen |
E188248
|
entity |
| Predicate | vehicleRegistrationCode |
P1173
|
FINISHED |
| Object |
MW
MW is the vehicle registration code used on license plates for cars registered in the District of Mittelsachsen in Germany.
|
E709523
|
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: MW | Statement: [District of Mittelsachsen, vehicleRegistrationCode, MW]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MW Context triple: [District of Mittelsachsen, vehicleRegistrationCode, MW]
-
A.
MW
MW is the two-letter ISO 3166-1 alpha-2 country code assigned to Malawi.
-
B.
WM
WM was the reporting mark for the Western Maryland Railway, a regional U.S. railroad that later became part of the Chessie System.
-
C.
MJ
MJ is the widely used nickname for Michael Jordan, the legendary American basketball player often regarded as the greatest in NBA history.
-
D.
MJ
MJ is a reimagined version of the Mary Jane Watson character who appears as Peter Parker’s sharp, observant classmate and love interest in the Marvel Cinematic Universe Spider-Man films.
-
E.
MJ
MJ is a Master of Jurisprudence graduate law degree designed for non-lawyers seeking advanced legal knowledge in a specific field.
- 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: MW Triple: [District of Mittelsachsen, vehicleRegistrationCode, MW]
Generated description
MW is the vehicle registration code used on license plates for cars registered in the District of Mittelsachsen in Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MW Target entity description: MW is the vehicle registration code used on license plates for cars registered in the District of Mittelsachsen in Germany.
-
A.
MW
MW is the two-letter ISO 3166-1 alpha-2 country code assigned to Malawi.
-
B.
WM
WM was the reporting mark for the Western Maryland Railway, a regional U.S. railroad that later became part of the Chessie System.
-
C.
MJ
MJ is the widely used nickname for Michael Jordan, the legendary American basketball player often regarded as the greatest in NBA history.
-
D.
MJ
MJ is a reimagined version of the Mary Jane Watson character who appears as Peter Parker’s sharp, observant classmate and love interest in the Marvel Cinematic Universe Spider-Man films.
-
E.
MJ
MJ is a Master of Jurisprudence graduate law degree designed for non-lawyers seeking advanced legal knowledge in a specific field.
- 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_69ca82b42674819086840efea12478e5 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3ff5547c8190a7ec5958a23e302f |
completed | March 31, 2026, 3:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc63e1ed44819083ed9db6c9d7b0fd |
completed | April 1, 2026, 12:16 a.m. |
| NEDg | Description generation | batch_69cc651c5f788190908c6d84c58cba0f |
completed | April 1, 2026, 12:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cc6649d2348190996802140b455348 |
completed | April 1, 2026, 12:26 a.m. |
Created at: March 30, 2026, 5:26 p.m.