Triple
T3647195
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sieg |
E77328
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object |
Siegburg
Siegburg is a historic town in North Rhine-Westphalia, Germany, known for its medieval abbey and location near Bonn and Cologne.
|
E377073
|
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: Siegburg | Statement: [Sieg, flowsThrough, Siegburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siegburg Context triple: [Sieg, flowsThrough, Siegburg]
-
A.
Siegen
Siegen is a city in western Germany known as the birthplace of the Baroque painter Peter Paul Rubens and for its historic mining and university traditions.
-
B.
Winsum
Winsum is a historic village and former municipality in the Dutch province of Groningen, known for its old churches, windmills, and picturesque canals.
-
C.
Hemfurth
Hemfurth is a village in central Germany best known for its proximity to the historic Eder Dam and the Edersee reservoir.
-
D.
Landsberg
Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
-
E.
Kleve
Kleve is a historic town in western Germany near the Dutch border, known for its medieval castle and role as the former capital of the Duchy of Cleves.
- 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: Siegburg Triple: [Sieg, flowsThrough, Siegburg]
Generated description
Siegburg is a historic town in North Rhine-Westphalia, Germany, known for its medieval abbey and location near Bonn and Cologne.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Siegburg Target entity description: Siegburg is a historic town in North Rhine-Westphalia, Germany, known for its medieval abbey and location near Bonn and Cologne.
-
A.
Siegen
Siegen is a city in western Germany known as the birthplace of the Baroque painter Peter Paul Rubens and for its historic mining and university traditions.
-
B.
Winsum
Winsum is a historic village and former municipality in the Dutch province of Groningen, known for its old churches, windmills, and picturesque canals.
-
C.
Hemfurth
Hemfurth is a village in central Germany best known for its proximity to the historic Eder Dam and the Edersee reservoir.
-
D.
Landsberg
Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
-
E.
Kleve
Kleve is a historic town in western Germany near the Dutch border, known for its medieval castle and role as the former capital of the Duchy of Cleves.
- 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_69ad85de1b988190a45f8dbfebc806fc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc38aa2388190bf1af926375e2433 |
completed | March 8, 2026, 6:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b48836f5d08190bbf0b6410ed6f766 |
completed | March 13, 2026, 9:57 p.m. |
| NEDg | Description generation | batch_69b48afc27d48190b8ae34f3b167ce79 |
completed | March 13, 2026, 10:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4a3ee82b88190a7dd5fceff04728c |
completed | March 13, 2026, 11:55 p.m. |
Created at: March 8, 2026, 3:24 p.m.