Triple
T17767883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Löstertal |
E443556
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object |
town of Wadern
The town of Wadern is a municipality in the Saarland region of Germany, known for its rural character and several incorporated villages.
|
E1286821
|
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: town of Wadern | Statement: [Löstertal, partOf, town of Wadern]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: town of Wadern Context triple: [Löstertal, partOf, town of Wadern]
-
A.
Rodewisch
Rodewisch is a small town in the Vogtland region of Saxony, Germany, known for its scenic surroundings and local industry.
-
B.
Wolfach
Wolfach is a small town in the Black Forest region of Baden-Württemberg, Germany, known for its traditional glassmaking and picturesque setting along the Kinzig River.
-
C.
Würselen
Würselen is a town in western Germany’s state of North Rhine-Westphalia, located near the city of Aachen and known historically for its mining and industrial heritage.
-
D.
Willebadessen
Willebadessen is a small town in western Germany, located in the state of North Rhine-Westphalia.
-
E.
Waldstadt
Waldstadt is a district of Karlsruhe in the German state of Baden-Württemberg, characterized by its forested setting and primarily residential layout.
- 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: town of Wadern Triple: [Löstertal, partOf, town of Wadern]
Generated description
The town of Wadern is a municipality in the Saarland region of Germany, known for its rural character and several incorporated villages.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: town of Wadern Target entity description: The town of Wadern is a municipality in the Saarland region of Germany, known for its rural character and several incorporated villages.
-
A.
Rodewisch
Rodewisch is a small town in the Vogtland region of Saxony, Germany, known for its scenic surroundings and local industry.
-
B.
Wolfach
Wolfach is a small town in the Black Forest region of Baden-Württemberg, Germany, known for its traditional glassmaking and picturesque setting along the Kinzig River.
-
C.
Würselen
Würselen is a town in western Germany’s state of North Rhine-Westphalia, located near the city of Aachen and known historically for its mining and industrial heritage.
-
D.
Willebadessen
Willebadessen is a small town in western Germany, located in the state of North Rhine-Westphalia.
-
E.
Waldstadt
Waldstadt is a district of Karlsruhe in the German state of Baden-Württemberg, characterized by its forested setting and primarily residential layout.
- 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_69d8b9edf16c8190a59ebd245d378f4f |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e485fd8aa081909c8ee9264af15c69 |
completed | April 19, 2026, 7:36 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a02efc14808819099ecbb8a752aff24 |
completed | May 12, 2026, 9:15 a.m. |
| NEDg | Description generation | batch_6a02f096fc648190a1ff19f2704a4cef |
completed | May 12, 2026, 9:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a02f1d0a4708190bbf51356da7e965e |
completed | May 12, 2026, 9:24 a.m. |
Created at: April 10, 2026, 10:11 a.m.