Triple
T11878516
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siege of Godesberg (1583) |
E282591
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object |
Godesberg
Godesberg is a historic district in Bonn, Germany, known for its medieval castle ruins and role in regional conflicts such as the Cologne War.
|
E980026
|
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: Godesberg | Statement: [Siege of Godesberg (1583), location, Godesberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Godesberg Context triple: [Siege of Godesberg (1583), location, Godesberg]
-
A.
Landsberg
Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
-
B.
Jülich
Jülich is a historic town in western Germany, known for its former status as a ducal residence and its significant Renaissance-era fortifications.
-
C.
Gescher
Gescher is a small town in western Germany’s Münsterland region, noted for its traditional bell foundries and rural character.
-
D.
Karlshagen
Karlshagen is a seaside resort village on the Baltic coast of northeastern Germany, located on the island of Usedom and known for its sandy beaches and tourism.
-
E.
Riemst
Riemst is a municipality in the Belgian province of Limburg, known for its rural character and location near the borders with the Netherlands and 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: Godesberg Triple: [Siege of Godesberg (1583), location, Godesberg]
Generated description
Godesberg is a historic district in Bonn, Germany, known for its medieval castle ruins and role in regional conflicts such as the Cologne War.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Godesberg Target entity description: Godesberg is a historic district in Bonn, Germany, known for its medieval castle ruins and role in regional conflicts such as the Cologne War.
-
A.
Landsberg
Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
-
B.
Jülich
Jülich is a historic town in western Germany, known for its former status as a ducal residence and its significant Renaissance-era fortifications.
-
C.
Gescher
Gescher is a small town in western Germany’s Münsterland region, noted for its traditional bell foundries and rural character.
-
D.
Karlshagen
Karlshagen is a seaside resort village on the Baltic coast of northeastern Germany, located on the island of Usedom and known for its sandy beaches and tourism.
-
E.
Riemst
Riemst is a municipality in the Belgian province of Limburg, known for its rural character and location near the borders with the Netherlands and 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_69d6ab2945d081908a5851c916cbcfb5 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8be1b6a5c81909a18c54205dda09c |
completed | April 10, 2026, 9:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6344d86648190ad270517b36c8815 |
completed | May 2, 2026, 5:28 p.m. |
| NEDg | Description generation | batch_69f6356b545c819089a5f5b901afc5f2 |
completed | May 2, 2026, 5:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f636382ffc8190becfae41757a45d8 |
completed | May 2, 2026, 5:36 p.m. |
Created at: April 8, 2026, 9:44 p.m.