Triple
T17534677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Venlo railway station |
E427026
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Mönchengladbach |
—
|
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: Mönchengladbach | Statement: [Venlo railway station, connectsTo, Mönchengladbach]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mönchengladbach Context triple: [Venlo railway station, connectsTo, Mönchengladbach]
-
A.
Mönchengladbach
chosen
Mönchengladbach is a city in western Germany known for its textile industry heritage and its football club Borussia Mönchengladbach.
-
B.
Bergisch Gladbach
Bergisch Gladbach is a city in North Rhine-Westphalia, western Germany, known for its paper industry, proximity to Cologne, and surrounding Bergisches Land countryside.
-
C.
Dortmund
Dortmund is a major city in western Germany known for its rich football culture, industrial heritage, and home club Borussia Dortmund.
-
D.
Krefeld
Krefeld is a city in western Germany near the Rhine River, known historically for its textile and silk industry.
-
E.
Gelsenkirchen
Gelsenkirchen is a city in western Germany known for its strong football culture and modern stadium, Veltins-Arena, home to FC Schalke 04.
- 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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4536ac7f48190994f7b39a6a811d7 |
completed | April 19, 2026, 4 a.m. |
Created at: April 10, 2026, 5:49 a.m.