Triple
T7481566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Koszalin |
E176769
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object |
Schwedt
Schwedt is a town in northeastern Germany, located on the Oder River near the Polish border, known for its industrial facilities and cross-border regional ties.
|
E689416
|
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: Schwedt | Statement: [Koszalin, twinTown, Schwedt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schwedt Context triple: [Koszalin, twinTown, Schwedt]
-
A.
Wandlitz
Wandlitz is a municipality in the German state of Brandenburg, known for its lakes, forests, and proximity to Berlin.
-
B.
Zinnowitz
Zinnowitz is a seaside resort town on Germany’s Baltic Sea coast, known for its sandy beaches, historic spa architecture, and tourism on the island of Usedom.
-
C.
Riesa
Riesa is a town in the German state of Saxony, situated on the Elbe River and known historically as an important regional railway and industrial center.
-
D.
Teterow
Teterow is a small historic town in northeastern Germany known for its medieval architecture and location in the Mecklenburg Lake District.
-
E.
Glienicke
Glienicke is a historic area in Berlin, Germany, known for its palaces, parks, and its location near the Glienicke Bridge over the Havel River.
- 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: Schwedt Triple: [Koszalin, twinTown, Schwedt]
Generated description
Schwedt is a town in northeastern Germany, located on the Oder River near the Polish border, known for its industrial facilities and cross-border regional ties.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Schwedt Target entity description: Schwedt is a town in northeastern Germany, located on the Oder River near the Polish border, known for its industrial facilities and cross-border regional ties.
-
A.
Wandlitz
Wandlitz is a municipality in the German state of Brandenburg, known for its lakes, forests, and proximity to Berlin.
-
B.
Zinnowitz
Zinnowitz is a seaside resort town on Germany’s Baltic Sea coast, known for its sandy beaches, historic spa architecture, and tourism on the island of Usedom.
-
C.
Riesa
Riesa is a town in the German state of Saxony, situated on the Elbe River and known historically as an important regional railway and industrial center.
-
D.
Teterow
Teterow is a small historic town in northeastern Germany known for its medieval architecture and location in the Mecklenburg Lake District.
-
E.
Glienicke
Glienicke is a historic area in Berlin, Germany, known for its palaces, parks, and its location near the Glienicke Bridge over the Havel River.
- 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_69c69f236ce08190a04d7679f03b29b2 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f534aa388190b3bb3e16be3a54c8 |
completed | March 27, 2026, 9:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8fa20137081909a21ac366c19407f |
completed | March 29, 2026, 10:08 a.m. |
| NEDg | Description generation | batch_69c8fde9ff1c81909aaae20da6c18a50 |
completed | March 29, 2026, 10:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8fe364fb081908cf0959809c204e2 |
completed | March 29, 2026, 10:25 a.m. |
Created at: March 27, 2026, 3:42 p.m.