Triple
T19598178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Julius Kühn Institute |
E470400
|
entity |
| Predicate | hasOfficeLocation |
P1268
|
FINISHED |
| Object |
Dossenheim
Dossenheim is a municipality in southwestern Germany near Heidelberg, known for its agricultural research facilities and scenic location along the Bergstraße.
|
E1399319
|
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: Dossenheim | Statement: [Julius Kühn Institute, hasOfficeLocation, Dossenheim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dossenheim Context triple: [Julius Kühn Institute, hasOfficeLocation, Dossenheim]
-
A.
Dettenheim
Dettenheim is a municipality in the Karlsruhe district of Baden-Württemberg, Germany, situated along the Pfinz river.
-
B.
Ottmarsheim
Ottmarsheim is a commune in northeastern France’s Alsace region, known for its historic Romanesque church and location along the Rhine.
-
C.
Ostelsheim
Ostelsheim is a small municipality in the northern Black Forest region of Baden-Württemberg in southwestern Germany.
-
D.
Beutelsbach
Beutelsbach is a small municipality in the rural Passau district of Lower Bavaria in southeastern Germany.
-
E.
Kaulsdorf
Kaulsdorf is a residential locality in eastern Berlin, Germany, known for its mix of historic village center and post-war housing estates.
- 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: Dossenheim Triple: [Julius Kühn Institute, hasOfficeLocation, Dossenheim]
Generated description
Dossenheim is a municipality in southwestern Germany near Heidelberg, known for its agricultural research facilities and scenic location along the Bergstraße.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dossenheim Target entity description: Dossenheim is a municipality in southwestern Germany near Heidelberg, known for its agricultural research facilities and scenic location along the Bergstraße.
-
A.
Dettenheim
Dettenheim is a municipality in the Karlsruhe district of Baden-Württemberg, Germany, situated along the Pfinz river.
-
B.
Ottmarsheim
Ottmarsheim is a commune in northeastern France’s Alsace region, known for its historic Romanesque church and location along the Rhine.
-
C.
Ostelsheim
Ostelsheim is a small municipality in the northern Black Forest region of Baden-Württemberg in southwestern Germany.
-
D.
Beutelsbach
Beutelsbach is a small municipality in the rural Passau district of Lower Bavaria in southeastern Germany.
-
E.
Kaulsdorf
Kaulsdorf is a residential locality in eastern Berlin, Germany, known for its mix of historic village center and post-war housing estates.
- 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_69d8e510024481908415c0d616fa6186 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e6407c52c081908704d3a4dd6e853b |
completed | April 20, 2026, 3:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07dbaca6608190b90f031529133a95 |
completed | May 16, 2026, 2:51 a.m. |
| NEDg | Description generation | batch_6a07dc950cd8819084a9184a20a15b85 |
completed | May 16, 2026, 2:55 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07dd016aa88190890d6272e51c30e2 |
completed | May 16, 2026, 2:57 a.m. |
Created at: April 10, 2026, 1:43 p.m.