Triple
T17050210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lauda-Königshofen |
E413673
|
entity |
| Predicate | hasCityPart |
P12399
|
FINISHED |
| Object |
Sachsenflur
Sachsenflur is a district of the town Lauda-Königshofen in the Main-Tauber region of Baden-Württemberg, Germany.
|
E1248441
|
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: Sachsenflur | Statement: [Lauda-Königshofen, hasCityPart, Sachsenflur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sachsenflur Context triple: [Lauda-Königshofen, hasCityPart, Sachsenflur]
-
A.
Schwanfeld
Schwanfeld is a small municipality in the Lower Franconia region of Bavaria, Germany, known for its rural character and historical roots.
-
B.
Fläming
Fläming is a low mountain and heathland region in eastern Germany known for its forests, rolling hills, and historic towns.
-
C.
Mühlenbecker Land
Mühlenbecker Land is a municipality in the Oberhavel district of Brandenburg, Germany, known for its proximity to Berlin and its mix of forests, lakes, and residential areas.
-
D.
Sulzthal
Sulzthal is a small municipality in the Bad Kissingen district of northern Bavaria, Germany, known for its rural character and Franconian cultural setting.
-
E.
Saale-Holzland region
The Saale-Holzland region is a rural district in the German state of Thuringia, known for its Saale River landscapes, forests, and small historic towns.
- 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: Sachsenflur Triple: [Lauda-Königshofen, hasCityPart, Sachsenflur]
Generated description
Sachsenflur is a district of the town Lauda-Königshofen in the Main-Tauber region of Baden-Württemberg, Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sachsenflur Target entity description: Sachsenflur is a district of the town Lauda-Königshofen in the Main-Tauber region of Baden-Württemberg, Germany.
-
A.
Schwanfeld
Schwanfeld is a small municipality in the Lower Franconia region of Bavaria, Germany, known for its rural character and historical roots.
-
B.
Fläming
Fläming is a low mountain and heathland region in eastern Germany known for its forests, rolling hills, and historic towns.
-
C.
Mühlenbecker Land
Mühlenbecker Land is a municipality in the Oberhavel district of Brandenburg, Germany, known for its proximity to Berlin and its mix of forests, lakes, and residential areas.
-
D.
Sulzthal
Sulzthal is a small municipality in the Bad Kissingen district of northern Bavaria, Germany, known for its rural character and Franconian cultural setting.
-
E.
Saale-Holzland region
The Saale-Holzland region is a rural district in the German state of Thuringia, known for its Saale River landscapes, forests, and small historic towns.
- 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_69d886cde3d481908d4d01ba88ba7eb7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3daa1aeac81909e8d97bd708c6b71 |
completed | April 18, 2026, 7:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a012341b8e88190a2bee865be5ca1c1 |
completed | May 11, 2026, 12:30 a.m. |
| NEDg | Description generation | batch_6a012585a1548190a112f55e2d84ccac |
completed | May 11, 2026, 12:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0126536c348190b9b2eadb4969f8c2 |
completed | May 11, 2026, 12:44 a.m. |
Created at: April 10, 2026, 5:34 a.m.