Triple
T18965197
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Autobahn A46 |
E464015
|
entity |
| Predicate | connectsCity |
P4245
|
FINISHED |
| Object | Bestwig |
—
|
NE NERFINISHED |
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: Bestwig | Statement: [Autobahn A46, connectsCity, Bestwig]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bestwig Context triple: [Autobahn A46, connectsCity, Bestwig]
-
A.
Bestwig
chosen
Bestwig is a municipality in the Hochsauerland district of North Rhine-Westphalia, Germany, known for its location in the Sauerland region and its scenic, hilly landscape.
-
B.
Wettig
Wettig is a surname most notably associated with American actress and playwright Patricia Wettig.
-
C.
Weigert
Weigert is a German-language surname borne by various notable individuals in fields such as science, sports, and the arts.
-
D.
Stahlecker
Stahlecker is a German-language surname most notably associated with Franz Walter Stahlecker, a high-ranking SS officer and Nazi official during World War II.
-
E.
Weinert
Weinert is a German-language surname borne by various notable individuals in fields such as the arts, sciences, and public life.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dcffc278819086792a4ebfddfafa |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d5d663948190b496fbd2e69c7f43 |
completed | April 20, 2026, 7:29 a.m. |
Created at: April 10, 2026, noon