Triple
T15898967
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bautzen district |
E385533
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Schönteichen
Schönteichen is a small municipality in the Free State of Saxony in eastern Germany.
|
E1228490
|
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: Schönteichen | Statement: [Bautzen district, containsTown, Schönteichen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schönteichen Context triple: [Bautzen district, containsTown, Schönteichen]
-
A.
Taufkirchen
Taufkirchen is a municipality in Bavaria, Germany, known for its strong aerospace and defense industry presence.
-
B.
Wilhelmsruh
Wilhelmsruh is a locality in the borough of Pankow in Berlin, Germany, known for its residential character and historical ties to Berlin’s former border zone.
-
C.
Fürstenzell
Fürstenzell is a market town and municipality in Lower Bavaria, Germany, known for its historic monastery and rural setting near the city of Passau.
-
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.
Trostberg
Trostberg is a small Bavarian town in southeastern Germany known for its historic old town and chemical industry.
- 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: Schönteichen Triple: [Bautzen district, containsTown, Schönteichen]
Generated description
Schönteichen is a small municipality in the Free State of Saxony in eastern Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Schönteichen Target entity description: Schönteichen is a small municipality in the Free State of Saxony in eastern Germany.
-
A.
Taufkirchen
Taufkirchen is a municipality in Bavaria, Germany, known for its strong aerospace and defense industry presence.
-
B.
Wilhelmsruh
Wilhelmsruh is a locality in the borough of Pankow in Berlin, Germany, known for its residential character and historical ties to Berlin’s former border zone.
-
C.
Fürstenzell
Fürstenzell is a market town and municipality in Lower Bavaria, Germany, known for its historic monastery and rural setting near the city of Passau.
-
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.
Trostberg
Trostberg is a small Bavarian town in southeastern Germany known for its historic old town and chemical industry.
- 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_69d86da5b800819083a31be937d738b0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1563bd0688190b6f7a695be0a4625 |
completed | April 16, 2026, 9:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a008a1f8d648190b9c6280b875a17e4 |
completed | May 10, 2026, 1:37 p.m. |
| NEDg | Description generation | batch_6a008ccaba388190afb9f17e20716d2e |
completed | May 10, 2026, 1:48 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a008d3c6fe081908c70e884eafb576f |
completed | May 10, 2026, 1:50 p.m. |
Created at: April 10, 2026, 4:51 a.m.