Triple
T14968764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wurm River |
E373260
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Herzogenrath |
E693691
|
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: Herzogenrath | Statement: [Wurm River, passesThrough, Herzogenrath]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Herzogenrath Context triple: [Wurm River, passesThrough, Herzogenrath]
-
A.
Herzogenrath
chosen
Herzogenrath is a town in western Germany near the Dutch border, known for its cross-border cooperation with the neighboring Dutch town of Kerkrade.
-
B.
Remscheid
Remscheid is a city in North Rhine-Westphalia, Germany, known historically for its metalworking industry and as the birthplace of physicist Wilhelm Röntgen.
-
C.
Rüttenscheid
Rüttenscheid is a lively, upscale district of Essen, Germany, known for its bustling shopping streets, restaurants, and cultural venues.
-
D.
Burscheid
Burscheid is a small town in North Rhine-Westphalia, Germany, known for its location in the hilly Bergisches Land region and its mix of rural character and local industry.
-
E.
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.
- 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_69d85ccbbcd48190acb56e7cf104d8ad |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6e44cb0819096e09f8026ef8174 |
completed | April 15, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffdbb7b1e48190b55c40e0cb837446 |
completed | May 10, 2026, 1:13 a.m. |
Created at: April 10, 2026, 2:48 a.m.