Triple
T8147867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erzgebirgskreis |
E190258
|
entity |
| Predicate | hasVehicleRegistrationCode |
P1173
|
FINISHED |
| Object |
MAB
MAB is a German vehicle registration code used for cars registered in the Erzgebirgskreis district of Saxony.
|
E717340
|
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: MAB | Statement: [Erzgebirgskreis, hasVehicleRegistrationCode, MAB]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MAB Context triple: [Erzgebirgskreis, hasVehicleRegistrationCode, MAB]
-
A.
MAB
MAB is a German bibliographic data format used for cataloging and exchanging library records, closely related to and historically aligned with MARC standards.
-
B.
MABBIM
MABBIM is a regional language council that coordinates and promotes the development and standardization of the Malay language across Brunei, Indonesia, and Malaysia.
-
C.
MAB Academy
MAB Academy is the training and development arm of Malaysia Aviation Group, providing aviation-related education and professional courses for airline and aviation industry personnel.
-
D.
MAD
MAD is a museum dedicated to contemporary art and design, showcasing innovative and experimental works across various media.
-
E.
MAD
MAD is the three-letter IATA airport code for Adolfo Suárez Madrid–Barajas Airport, the main international airport serving Madrid, Spain.
- 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: MAB Triple: [Erzgebirgskreis, hasVehicleRegistrationCode, MAB]
Generated description
MAB is a German vehicle registration code used for cars registered in the Erzgebirgskreis district of Saxony.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MAB Target entity description: MAB is a German vehicle registration code used for cars registered in the Erzgebirgskreis district of Saxony.
-
A.
MAB
MAB is a German bibliographic data format used for cataloging and exchanging library records, closely related to and historically aligned with MARC standards.
-
B.
MABBIM
MABBIM is a regional language council that coordinates and promotes the development and standardization of the Malay language across Brunei, Indonesia, and Malaysia.
-
C.
MAB Academy
MAB Academy is the training and development arm of Malaysia Aviation Group, providing aviation-related education and professional courses for airline and aviation industry personnel.
-
D.
MAD
MAD is a museum dedicated to contemporary art and design, showcasing innovative and experimental works across various media.
-
E.
MAD
MAD is the three-letter IATA airport code for Adolfo Suárez Madrid–Barajas Airport, the main international airport serving Madrid, Spain.
- 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_69ca82be7ba8819087de0147e9292c83 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb447e74e081908df774edb2134209 |
completed | March 31, 2026, 3:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccbee697208190a1d9c98b2a4414bd |
completed | April 1, 2026, 6:44 a.m. |
| NEDg | Description generation | batch_69ccc30f1fc48190991e0caa9ea6e735 |
completed | April 1, 2026, 7:02 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ccd8041c00819094094701ace21aa0 |
completed | April 1, 2026, 8:32 a.m. |
Created at: March 30, 2026, 5:36 p.m.