Triple
T22898888
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CH-SH |
E568254
|
entity |
| Predicate | highestPointName |
P210
|
FINISHED |
| Object |
Hagen (near Beggingen)
Hagen (near Beggingen) is a wooded hill in the Swiss canton of Schaffhausen known as the region’s highest elevation and a popular local viewpoint.
|
E1559625
|
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: Hagen (near Beggingen) | Statement: [CH-SH, highestPointName, Hagen (near Beggingen)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hagen (near Beggingen) Context triple: [CH-SH, highestPointName, Hagen (near Beggingen)]
-
A.
Reitzenhagen
Reitzenhagen is a district of the spa town Bad Wildungen in the state of Hesse, Germany.
-
B.
Hodenhagen
Hodenhagen is a small municipality in Lower Saxony, Germany, known for its rural setting along the Aller River and proximity to attractions like the Serengeti Park safari zoo.
-
C.
Hagen
Hagen is a city in the Ruhr region of North Rhine-Westphalia in western Germany, known historically as an industrial and transport hub.
-
D.
Hagen
Hagen is a small locality in northeastern France that forms part of the administrative area of the canton of Yutz in the Moselle department.
-
E.
Hagen
Hagen is a surname of German origin borne by various notable individuals across fields such as music, sports, and academia.
- 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: Hagen (near Beggingen) Triple: [CH-SH, highestPointName, Hagen (near Beggingen)]
Generated description
Hagen (near Beggingen) is a wooded hill in the Swiss canton of Schaffhausen known as the region’s highest elevation and a popular local viewpoint.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hagen (near Beggingen) Target entity description: Hagen (near Beggingen) is a wooded hill in the Swiss canton of Schaffhausen known as the region’s highest elevation and a popular local viewpoint.
-
A.
Reitzenhagen
Reitzenhagen is a district of the spa town Bad Wildungen in the state of Hesse, Germany.
-
B.
Hodenhagen
Hodenhagen is a small municipality in Lower Saxony, Germany, known for its rural setting along the Aller River and proximity to attractions like the Serengeti Park safari zoo.
-
C.
Hagen
Hagen is a city in the Ruhr region of North Rhine-Westphalia in western Germany, known historically as an industrial and transport hub.
-
D.
Hagen
Hagen is a surname of German origin borne by various notable individuals across fields such as music, sports, and academia.
-
E.
Hagen
Hagen is a formidable and cunning warrior in the medieval German epic "Nibelungenlied," best known for betraying and killing the hero Siegfried.
- 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_69e2458c23ec81908fa2570692c6614f |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1801415dc8190b5de6095f1ed4ba5 |
completed | April 29, 2026, 3:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0bb9c78b548190b65d3ca4f825b3db |
completed | May 19, 2026, 1:15 a.m. |
| NEDg | Description generation | batch_6a0bba7a90a081908c3d42be43e5f1f9 |
completed | May 19, 2026, 1:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0bbae04d1881909e652b74862b0644 |
completed | May 19, 2026, 1:20 a.m. |
Created at: April 17, 2026, 3:41 p.m.