Triple
T17157390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bundesstraße 51 |
E416378
|
entity |
| Predicate | providesAccessTo |
P1985
|
FINISHED |
| Object | Remscheid-Lennep |
E272299
|
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: Remscheid-Lennep | Statement: [Bundesstraße 51, providesAccessTo, Remscheid-Lennep]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Remscheid-Lennep Context triple: [Bundesstraße 51, providesAccessTo, Remscheid-Lennep]
-
A.
Rüttenscheid
Rüttenscheid is a lively, upscale district of Essen, Germany, known for its bustling shopping streets, restaurants, and cultural venues.
-
B.
Remscheid
chosen
Remscheid is a city in North Rhine-Westphalia, Germany, known historically for its metalworking industry and as the birthplace of physicist Wilhelm Röntgen.
-
C.
Lüdenscheid
Lüdenscheid is a town in western Germany’s Sauerland region, historically noted for its role in World War II and known today for its metal and plastics industries.
-
D.
Lünen
Lünen is a town in North Rhine-Westphalia, Germany, known as an industrial and commuter city in the Ruhr area.
-
E.
Raunheim
Raunheim is a town in the German state of Hesse, located near Frankfurt am Main and known for its proximity to major transportation routes and Frankfurt Airport.
- 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_69d886d279c081909f8ff1f743ddeb69 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3f40bf9ec8190b16372bcd091db9b |
completed | April 18, 2026, 9:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a015fc46c308190b09efb13776747e7 |
completed | May 11, 2026, 4:49 a.m. |
Created at: April 10, 2026, 5:37 a.m.