Triple
T10865134
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Goslar station |
E256506
|
entity |
| Predicate | connectsToCity |
P4245
|
FINISHED |
| Object | Vienenburg |
E312129
|
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: Vienenburg | Statement: [Goslar station, connectsToCity, Vienenburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vienenburg Context triple: [Goslar station, connectsToCity, Vienenburg]
-
A.
Vienenburg
chosen
Vienenburg is a district of Goslar in Lower Saxony, Germany, known for its historic town center and proximity to the Harz Mountains.
-
B.
Maichingen
Maichingen is a district of the city of Sindelfingen in the German state of Baden-Württemberg.
-
C.
Schwabhausen
Schwabhausen is a municipality in Bavaria, Germany, known for its rural character and location within the greater Munich metropolitan region.
-
D.
Rottweil
Rottweil is a historic town in southwestern Germany known for its medieval architecture and as the namesake of the Rottweiler dog breed.
-
E.
Biburg
Biburg is a small municipality in the Lower Bavarian region of Germany, known for its rural character and historic monastery.
- 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_69d6aa83d1448190a66d93c32394d21f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7516b2f148190adbacd35fc8c2056 |
completed | April 9, 2026, 7:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f12f646ec88190ab4745c52798b599 |
completed | April 28, 2026, 10:06 p.m. |
Created at: April 8, 2026, 9:20 p.m.