Triple
T9833933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marburg-Biedenkopf |
E239053
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Stadtallendorf
Stadtallendorf is a town in the German state of Hesse known for its industrial history and role as a regional economic center.
|
E824431
|
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: Stadtallendorf | Statement: [Marburg-Biedenkopf, containsTown, Stadtallendorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stadtallendorf Context triple: [Marburg-Biedenkopf, containsTown, Stadtallendorf]
-
A.
Westendorf
Westendorf is a popular Austrian alpine village known for its skiing, hiking, and picturesque mountain scenery.
-
B.
Burkhardtsdorf
Burkhardtsdorf is a small municipality in the Erzgebirge (Ore Mountains) region of Saxony, eastern Germany.
-
C.
Dierdorf
Dierdorf is a surname most prominently associated with former American football player and sportscaster Dan Dierdorf.
-
D.
Rhöndorf
Rhöndorf is a district of Bad Honnef in Germany, best known as the longtime residence and final home of the first Chancellor of the Federal Republic of Germany, Konrad Adenauer.
-
E.
Offendorf
Offendorf is a small commune in northeastern France’s Alsace region, situated along the Rhine and known for its riverside setting and traditional village character.
- 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: Stadtallendorf Triple: [Marburg-Biedenkopf, containsTown, Stadtallendorf]
Generated description
Stadtallendorf is a town in the German state of Hesse known for its industrial history and role as a regional economic center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stadtallendorf Target entity description: Stadtallendorf is a town in the German state of Hesse known for its industrial history and role as a regional economic center.
-
A.
Westendorf
Westendorf is a popular Austrian alpine village known for its skiing, hiking, and picturesque mountain scenery.
-
B.
Burkhardtsdorf
Burkhardtsdorf is a small municipality in the Erzgebirge (Ore Mountains) region of Saxony, eastern Germany.
-
C.
Dierdorf
Dierdorf is a surname most prominently associated with former American football player and sportscaster Dan Dierdorf.
-
D.
Rhöndorf
Rhöndorf is a district of Bad Honnef in Germany, best known as the longtime residence and final home of the first Chancellor of the Federal Republic of Germany, Konrad Adenauer.
-
E.
Offendorf
Offendorf is a small commune in northeastern France’s Alsace region, situated along the Rhine and known for its riverside setting and traditional village character.
- 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_69ca84e314108190978324a4bdb959f8 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3385054819094145c96204e3f0d |
completed | April 2, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1d5c448388190818e4cc5e3a42dfc |
completed | April 5, 2026, 3:23 a.m. |
| NEDg | Description generation | batch_69d1d6affc3c8190839a4db8f4271309 |
completed | April 5, 2026, 3:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1d772de00819089eed8be9f5ce3ce |
completed | April 5, 2026, 3:30 a.m. |
Created at: March 30, 2026, 8:32 p.m.