Triple
T6320459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bezirk Magdeburg |
E141723
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Gardelegen
Gardelegen is a historic town in the German state of Saxony-Anhalt, known for its medieval architecture and its location in the Altmark region.
|
E690448
|
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: Gardelegen | Statement: [Bezirk Magdeburg, contains, Gardelegen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gardelegen Context triple: [Bezirk Magdeburg, contains, Gardelegen]
-
A.
Lippstadt
Lippstadt is a historic town in North Rhine-Westphalia, Germany, known for its medieval architecture and role in regional conflicts.
-
B.
Detmold
Detmold is a historic town in northwestern Germany that served as the capital and residence city of the former Principality of Lippe.
-
C.
Bielefeld
Bielefeld is a major city in northwestern Germany known for its industrial heritage, university, and the tongue-in-cheek “Bielefeld conspiracy” meme claiming it does not exist.
-
D.
Gevelsberg
Gevelsberg is a town in North Rhine-Westphalia, Germany, situated in the Ennepe-Ruhr district within the Ruhr metropolitan region.
-
E.
Schmalkalde
Schmalkalde is a small river in central Germany that flows through the town of Schmalkalden before joining the Werra.
- 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: Gardelegen Triple: [Bezirk Magdeburg, contains, Gardelegen]
Generated description
Gardelegen is a historic town in the German state of Saxony-Anhalt, known for its medieval architecture and its location in the Altmark region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gardelegen Target entity description: Gardelegen is a historic town in the German state of Saxony-Anhalt, known for its medieval architecture and its location in the Altmark region.
-
A.
Lippstadt
Lippstadt is a historic town in North Rhine-Westphalia, Germany, known for its medieval architecture and role in regional conflicts.
-
B.
Detmold
Detmold is a historic town in northwestern Germany that served as the capital and residence city of the former Principality of Lippe.
-
C.
Bielefeld
Bielefeld is a major city in northwestern Germany known for its industrial heritage, university, and the tongue-in-cheek “Bielefeld conspiracy” meme claiming it does not exist.
-
D.
Gevelsberg
Gevelsberg is a town in North Rhine-Westphalia, Germany, situated in the Ennepe-Ruhr district within the Ruhr metropolitan region.
-
E.
Schmalkalde
Schmalkalde is a small river in central Germany that flows through the town of Schmalkalden before joining the Werra.
- 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_69c008d13b8c8190be47d896eb735605 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c064c61f008190b316b9ff1023b057 |
completed | March 22, 2026, 9:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c9379e5898819083bece0d129fed5c |
completed | March 29, 2026, 2:30 p.m. |
| NEDg | Description generation | batch_69c93896e0788190ab14cc289919bfd6 |
completed | March 29, 2026, 2:35 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c938e5e8b08190a1a4f4240036bb29 |
completed | March 29, 2026, 2:36 p.m. |
Created at: March 22, 2026, 4:29 p.m.