Triple
T13497056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bamberg–Hof railway |
E320787
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Hof Hauptbahnhof |
E806932
|
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: Hof Hauptbahnhof | Statement: [Bamberg–Hof railway, hasStation, Hof Hauptbahnhof]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hof Hauptbahnhof Context triple: [Bamberg–Hof railway, hasStation, Hof Hauptbahnhof]
-
A.
Hof Hauptbahnhof
chosen
Hof Hauptbahnhof is the main railway station serving the city of Hof in Bavaria, Germany, functioning as a regional transport hub.
-
B.
Bern Hauptbahnhof
Bern Hauptbahnhof is the main railway station in Switzerland’s capital city, serving as a major national and international transport hub.
-
C.
Hamm Hauptbahnhof
Hamm Hauptbahnhof is the central railway station and major rail hub serving the city of Hamm in North Rhine-Westphalia, Germany.
-
D.
Hagen Hauptbahnhof
Hagen Hauptbahnhof is the main railway station and central transport hub of the city of Hagen in North Rhine-Westphalia, Germany.
-
E.
Nürnberg Hauptbahnhof
Nürnberg Hauptbahnhof is the main railway station and a central public transport hub in Nuremberg, Germany, serving regional, long-distance, and urban transit including the U-Bahn network.
- 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_69d807629d6c8190998f1b9bb12d2ed0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaf4e9ca4819083116890a65389f9 |
completed | April 12, 2026, 2:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75d8e69a08190b74f1fecb07dd6e9 |
completed | May 3, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:43 p.m.