Triple
T2320498
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sd.Kfz. 251 |
E51167
|
entity |
| Predicate | manufacturer |
P490
|
FINISHED |
| Object | Büssing-NAG |
E212629
|
NE FINISHED |
How this triple was built (2 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: Büssing-NAG | Statement: [Sd.Kfz. 251, manufacturer, Büssing-NAG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Büssing-NAG Context triple: [Sd.Kfz. 251, manufacturer, Büssing-NAG]
-
A.
Borsigwerke
Borsigwerke is a Berlin U-Bahn station on line U6 serving the Tegel district in the city’s northwest.
-
B.
Erla Maschinenwerk
Erla Maschinenwerk was a German aircraft manufacturing company best known for producing Messerschmitt fighter planes under license during World War II.
-
C.
Krauss-Maffei Wegmann
Krauss-Maffei Wegmann is a German defense company specializing in the design and production of armored vehicles and military land systems.
-
D.
Maschinenfabrik Augsburg-Nürnberg
chosen
Maschinenfabrik Augsburg-Nürnberg was a major German engineering company best known as the predecessor of MAN SE, a leading manufacturer of commercial vehicles and industrial machinery.
-
E.
Bühler
Bühler is a German-language surname borne by various notable individuals across fields such as politics, sports, and academia.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a88b074b908190ae983dbca7757d88 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc632474c8190972b4611a3a4ff8f |
completed | March 7, 2026, 6:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae896911908190b53954dbf854cc18 |
completed | March 9, 2026, 8:48 a.m. |
Created at: March 4, 2026, 7:49 p.m.