Triple
T5348443
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bundesstraße 3 |
E124112
|
entity |
| Predicate | roadType |
P1019
|
FINISHED |
| Object | Bundesstraße |
E303466
|
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: Bundesstraße | Statement: [Bundesstraße 3, roadType, Bundesstraße]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bundesstraße Context triple: [Bundesstraße 3, roadType, Bundesstraße]
-
A.
Bundesstraße
chosen
A Bundesstraße is a major federal highway in Germany that connects cities and regions and is ranked below the Autobahn in the national road network.
-
B.
Bundesstraße 10
Bundesstraße 10 is a major federal highway in southern Germany that runs east–west and connects several important cities in the states of Baden-Württemberg and Rhineland-Palatinate.
-
C.
Bundesstraße 7
Bundesstraße 7 is a major German federal highway running east–west across several states and connecting numerous cities and regions.
-
D.
Bundesstraße 3
Bundesstraße 3 is a major German federal highway running north–south through several states and connecting numerous cities and towns.
-
E.
Bundesstraße 8
Bundesstraße 8 is a major German federal highway running east–west through several states and connecting numerous towns and cities.
- 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_69bd464be27081908807b40b75c1bbae |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd85ef75148190815461c2a49302e9 |
completed | March 20, 2026, 5:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf21cd34a08190857dd960c12fec0c |
completed | March 21, 2026, 10:55 p.m. |
Created at: March 20, 2026, 2:01 p.m.