Triple
T10130029
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Markkleeberg |
E226311
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object |
Böhlen
Böhlen is a small town in the Leipzig district of Saxony, Germany, known for its lignite mining and power generation industries.
|
E921039
|
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: Böhlen | Statement: [Markkleeberg, adjacentTo, Böhlen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Böhlen Context triple: [Markkleeberg, adjacentTo, Böhlen]
-
A.
Borgholzhausen
Borgholzhausen is a small town in North Rhine-Westphalia, Germany, known for its location on the Teutoburg Forest and its historical ties to the former County of Ravensberg.
-
B.
Kulmbach
Kulmbach is a historic Bavarian town in northern Germany renowned for its beer brewing tradition and its hilltop Plassenburg Castle.
-
C.
Trostberg
Trostberg is a small Bavarian town in southeastern Germany known for its historic old town and chemical industry.
-
D.
Zusenhofen
Zusenhofen is a village and district within the town of Oberkirch in the Ortenau region of Baden-Württemberg, Germany.
-
E.
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.
- 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: Böhlen Triple: [Markkleeberg, adjacentTo, Böhlen]
Generated description
Böhlen is a small town in the Leipzig district of Saxony, Germany, known for its lignite mining and power generation industries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Böhlen Target entity description: Böhlen is a small town in the Leipzig district of Saxony, Germany, known for its lignite mining and power generation industries.
-
A.
Borgholzhausen
Borgholzhausen is a small town in North Rhine-Westphalia, Germany, known for its location on the Teutoburg Forest and its historical ties to the former County of Ravensberg.
-
B.
Kulmbach
Kulmbach is a historic Bavarian town in northern Germany renowned for its beer brewing tradition and its hilltop Plassenburg Castle.
-
C.
Trostberg
Trostberg is a small Bavarian town in southeastern Germany known for its historic old town and chemical industry.
-
D.
Zusenhofen
Zusenhofen is a village and district within the town of Oberkirch in the Ortenau region of Baden-Württemberg, Germany.
-
E.
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.
- 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_69ca843057b48190a86730167f5d6b98 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cdd33438988190be45878f98695816 |
completed | April 2, 2026, 2:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5561714a081909cbf1cc7d5d0ac0a |
completed | April 19, 2026, 10:24 p.m. |
| NEDg | Description generation | batch_69e562bb085c8190942766d12d838798 |
completed | April 19, 2026, 11:18 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e569f0e6948190b285ca84aca03771 |
completed | April 19, 2026, 11:49 p.m. |
Created at: March 30, 2026, 9:05 p.m.