Triple
T8525054
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Böbing |
E201791
|
entity |
| Predicate | hasVehicleRegistrationCode |
P1173
|
FINISHED |
| Object |
WM
WM is the vehicle registration code for the district of Weilheim-Schongau in Bavaria, Germany.
|
E739090
|
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: WM | Statement: [Böbing, hasVehicleRegistrationCode, WM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: WM Context triple: [Böbing, hasVehicleRegistrationCode, WM]
-
A.
WM
WM was the reporting mark for the Western Maryland Railway, a regional U.S. railroad that later became part of the Chessie System.
-
B.
WN
WN is the IATA airline designator used to identify Southwest Airlines in flight schedules, ticketing, and aviation operations.
-
C.
WN
WN is the vehicle registration code used on license plates for the Waiblingen district in the German state of Baden-Württemberg.
-
D.
WL
WL is the station code for Lutherstadt Wittenberg railway station in Germany.
-
E.
MW
MW is the vehicle registration code used on license plates for cars registered in the District of Mittelsachsen in Germany.
- 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: WM Triple: [Böbing, hasVehicleRegistrationCode, WM]
Generated description
WM is the vehicle registration code for the district of Weilheim-Schongau in Bavaria, Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: WM Target entity description: WM is the vehicle registration code for the district of Weilheim-Schongau in Bavaria, Germany.
-
A.
WM
WM was the reporting mark for the Western Maryland Railway, a regional U.S. railroad that later became part of the Chessie System.
-
B.
WN
WN is the IATA airline designator used to identify Southwest Airlines in flight schedules, ticketing, and aviation operations.
-
C.
WN
WN is the vehicle registration code used on license plates for the Waiblingen district in the German state of Baden-Württemberg.
-
D.
WL
WL is the station code for Lutherstadt Wittenberg railway station in Germany.
-
E.
MW
MW is the vehicle registration code used on license plates for cars registered in the District of Mittelsachsen in Germany.
- 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_69ca83228b24819085d22e7dc99f5d94 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe644c4648190a14dcaeaa90d72c7 |
completed | March 31, 2026, 3:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce4e9219108190979430a4308fc4c5 |
completed | April 2, 2026, 11:10 a.m. |
| NEDg | Description generation | batch_69ce5028c38c81908e81390d0be21387 |
completed | April 2, 2026, 11:16 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ce517fea7c819098d0343fd2c4ccdd |
completed | April 2, 2026, 11:22 a.m. |
Created at: March 30, 2026, 6:16 p.m.