Triple
T18965182
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Autobahn A46 |
E464015
|
entity |
| Predicate | connectsCity |
P4245
|
FINISHED |
| Object |
Hückelhoven
Hückelhoven is a town in western Germany’s North Rhine-Westphalia region, known for its location in the Rhineland and its historical ties to coal mining.
|
E1370446
|
NE FINISHED |
How this triple was built (4 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: Hückelhoven | Statement: [Autobahn A46, connectsCity, Hückelhoven]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hückelhoven Context triple: [Autobahn A46, connectsCity, Hückelhoven]
-
A.
Hückeswagen
Hückeswagen is a small historic town in western Germany’s North Rhine-Westphalia, known for its medieval castle and location in the hilly Bergisches Land region.
-
B.
Rüdinghausen
Rüdinghausen is a district of the city of Witten in North Rhine-Westphalia, Germany, characterized by its residential areas and local amenities.
-
C.
Nörtershausen
Nörtershausen is a small municipality in western Germany’s Rhineland-Palatinate region, situated in the Rhine-Mosel area.
-
D.
Espelkamp
Espelkamp is a small town in North Rhine-Westphalia, Germany, known for its post-war planned layout and light industrial economy.
-
E.
Hornau
Hornau is a district of the town of Kelkheim in the Main-Taunus region of Hesse, Germany.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Hückelhoven Triple: [Autobahn A46, connectsCity, Hückelhoven]
Generated description
Hückelhoven is a town in western Germany’s North Rhine-Westphalia region, known for its location in the Rhineland and its historical ties to coal mining.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hückelhoven Target entity description: Hückelhoven is a town in western Germany’s North Rhine-Westphalia region, known for its location in the Rhineland and its historical ties to coal mining.
-
A.
Hückeswagen
Hückeswagen is a small historic town in western Germany’s North Rhine-Westphalia, known for its medieval castle and location in the hilly Bergisches Land region.
-
B.
Rüdinghausen
Rüdinghausen is a district of the city of Witten in North Rhine-Westphalia, Germany, characterized by its residential areas and local amenities.
-
C.
Nörtershausen
Nörtershausen is a small municipality in western Germany’s Rhineland-Palatinate region, situated in the Rhine-Mosel area.
-
D.
Espelkamp
Espelkamp is a small town in North Rhine-Westphalia, Germany, known for its post-war planned layout and light industrial economy.
-
E.
Hornau
Hornau is a district of the town of Kelkheim in the Main-Taunus region of Hesse, Germany.
- F. None of above. chosen
Provenance (5 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_69d8dcffc278819086792a4ebfddfafa |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d5d663948190b496fbd2e69c7f43 |
completed | April 20, 2026, 7:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a071bc49a5c8190b25a20023a3e521d |
completed | May 15, 2026, 1:12 p.m. |
| NEDg | Description generation | batch_6a071ca19f308190ac9072c5a8d3b665 |
completed | May 15, 2026, 1:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a071de35b9081908d1b6e797cbe69ce |
completed | May 15, 2026, 1:21 p.m. |
Created at: April 10, 2026, noon