Triple
T2340543
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eder Dam |
E45015
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Hemfurth
Hemfurth is a village in central Germany best known for its proximity to the historic Eder Dam and the Edersee reservoir.
|
E261051
|
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: Hemfurth | Statement: [Eder Dam, locatedNear, Hemfurth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hemfurth Context triple: [Eder Dam, locatedNear, Hemfurth]
-
A.
Merseburg
Merseburg is a historic town in the German state of Saxony-Anhalt, known for its medieval cathedral and role as an important cultural and administrative center on the River Saale.
-
B.
Drensteinfurt
Drensteinfurt is a small town in North Rhine-Westphalia, Germany, known for its historic architecture and location in the Münsterland region.
-
C.
Lankwitz
Lankwitz is a residential locality in the southwestern part of Berlin, known for its quiet neighborhoods, green spaces, and mix of historic and modern architecture.
-
D.
Wallhausen
Wallhausen is a village in present-day Saxony-Anhalt, Germany, historically notable as the birthplace of Otto I, Holy Roman Emperor.
-
E.
Gifhorn
Gifhorn is a town in Lower Saxony, Germany, known for its location near the confluence of several rivers and its historic windmill museum.
- 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: Hemfurth Triple: [Eder Dam, locatedNear, Hemfurth]
Generated description
Hemfurth is a village in central Germany best known for its proximity to the historic Eder Dam and the Edersee reservoir.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hemfurth Target entity description: Hemfurth is a village in central Germany best known for its proximity to the historic Eder Dam and the Edersee reservoir.
-
A.
Merseburg
Merseburg is a historic town in the German state of Saxony-Anhalt, known for its medieval cathedral and role as an important cultural and administrative center on the River Saale.
-
B.
Drensteinfurt
Drensteinfurt is a small town in North Rhine-Westphalia, Germany, known for its historic architecture and location in the Münsterland region.
-
C.
Lankwitz
Lankwitz is a residential locality in the southwestern part of Berlin, known for its quiet neighborhoods, green spaces, and mix of historic and modern architecture.
-
D.
Wallhausen
Wallhausen is a village in present-day Saxony-Anhalt, Germany, historically notable as the birthplace of Otto I, Holy Roman Emperor.
-
E.
Gifhorn
Gifhorn is a town in Lower Saxony, Germany, known for its location near the confluence of several rivers and its historic windmill museum.
- 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_69a88917935081909b755dbf38e81024 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abc6abf1688190b7c404de0979149a |
completed | March 7, 2026, 6:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aea87cbe688190a97a4c11c46f9f54 |
completed | March 9, 2026, 11:01 a.m. |
| NEDg | Description generation | batch_69aeaa6cb58081909a0897d4cb328063 |
completed | March 9, 2026, 11:09 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69aeabb6d43481908899080f6ca58101 |
completed | March 9, 2026, 11:15 a.m. |
Created at: March 4, 2026, 7:52 p.m.