Triple
T6406618
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regensburg |
E127596
|
entity |
| Predicate | hasLandmark |
P105
|
FINISHED |
| Object |
Stadtamhof
Stadtamhof is a historic district of Regensburg, Germany, located on an island in the Danube and known for its well-preserved medieval architecture and UNESCO World Heritage status.
|
E598798
|
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: Stadtamhof | Statement: [Regensburg, hasLandmark, Stadtamhof]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stadtamhof Context triple: [Regensburg, hasLandmark, Stadtamhof]
-
A.
Willstätt
Willstätt is a municipality in southwestern Germany’s Baden-Württemberg region, situated near the Rhine and the French border.
-
B.
Taufkirchen
Taufkirchen is a municipality in Bavaria, Germany, known for its strong aerospace and defense industry presence.
-
C.
Hubersdorf
Hubersdorf is a small municipality located in the canton of Solothurn in northwestern Switzerland.
-
D.
Hettstadt
Hettstadt is a small municipality in the Würzburg district of Bavaria, Germany, known for its rural character and proximity to the city of Würzburg.
-
E.
Hasselfelde
Hasselfelde is a small town in the Harz region of central Germany, now incorporated into the municipality of Oberharz am Brocken.
- 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: Stadtamhof Triple: [Regensburg, hasLandmark, Stadtamhof]
Generated description
Stadtamhof is a historic district of Regensburg, Germany, located on an island in the Danube and known for its well-preserved medieval architecture and UNESCO World Heritage status.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stadtamhof Target entity description: Stadtamhof is a historic district of Regensburg, Germany, located on an island in the Danube and known for its well-preserved medieval architecture and UNESCO World Heritage status.
-
A.
Willstätt
Willstätt is a municipality in southwestern Germany’s Baden-Württemberg region, situated near the Rhine and the French border.
-
B.
Taufkirchen
Taufkirchen is a municipality in Bavaria, Germany, known for its strong aerospace and defense industry presence.
-
C.
Hubersdorf
Hubersdorf is a small municipality located in the canton of Solothurn in northwestern Switzerland.
-
D.
Hettstadt
Hettstadt is a small municipality in the Würzburg district of Bavaria, Germany, known for its rural character and proximity to the city of Würzburg.
-
E.
Hasselfelde
Hasselfelde is a small town in the Harz region of central Germany, now incorporated into the municipality of Oberharz am Brocken.
- 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_69c0083723d88190b1e37b19df162c08 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c068b3541c8190be89b24b313d7300 |
completed | March 22, 2026, 10:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c685748e808190a540d9f99cd58a8a |
completed | March 27, 2026, 1:26 p.m. |
| NEDg | Description generation | batch_69c6994dafac819097586bd23aee35c4 |
completed | March 27, 2026, 2:50 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6ac08698c8190b8a0a9625492353b |
completed | March 27, 2026, 4:10 p.m. |
Created at: March 22, 2026, 4:41 p.m.