Triple
T12877901
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leipzig metropolitan region |
E308012
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Priestewitz
Priestewitz is a small municipality in the German state of Saxony that lies within the broader Leipzig metropolitan region.
|
E1013719
|
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: Priestewitz | Statement: [Leipzig metropolitan region, containsCity, Priestewitz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Priestewitz Context triple: [Leipzig metropolitan region, containsCity, Priestewitz]
-
A.
Seelitz
Seelitz is a municipality in the Free State of Saxony in eastern Germany, known for its rural character and location within the Mittelsachsen region.
-
B.
Lippendorf
Lippendorf is a village in Saxony, Germany, historically notable as the birthplace of Katharina von Bora, the wife of Martin Luther.
-
C.
Delitzsch
Delitzsch is a historic town in the German state of Saxony, known for its well-preserved medieval center and regional administrative role.
-
D.
Biesenthal
Biesenthal is a small town in the Barnim district of Brandenburg, Germany, known for its surrounding lakes, forests, and location within the Barnim Nature Park.
-
E.
Lilienthal
Lilienthal is a German-origin surname borne by various notable individuals, including figures in aviation, science, and public service.
- 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: Priestewitz Triple: [Leipzig metropolitan region, containsCity, Priestewitz]
Generated description
Priestewitz is a small municipality in the German state of Saxony that lies within the broader Leipzig metropolitan region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Priestewitz Target entity description: Priestewitz is a small municipality in the German state of Saxony that lies within the broader Leipzig metropolitan region.
-
A.
Seelitz
Seelitz is a municipality in the Free State of Saxony in eastern Germany, known for its rural character and location within the Mittelsachsen region.
-
B.
Lippendorf
Lippendorf is a village in Saxony, Germany, historically notable as the birthplace of Katharina von Bora, the wife of Martin Luther.
-
C.
Delitzsch
Delitzsch is a historic town in the German state of Saxony, known for its well-preserved medieval center and regional administrative role.
-
D.
Biesenthal
Biesenthal is a small town in the Barnim district of Brandenburg, Germany, known for its surrounding lakes, forests, and location within the Barnim Nature Park.
-
E.
Lilienthal
Lilienthal is a German-origin surname borne by various notable individuals, including figures in aviation, science, and public service.
- 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_69d7bdf69bc48190af6c2621f28ca351 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d970fa8474819086a8af3c90f3ca84 |
completed | April 10, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6b8ccee708190bb4caa604386e3a3 |
completed | May 3, 2026, 2:54 a.m. |
| NEDg | Description generation | batch_69f6bafee83c819096469034ca32ff7d |
completed | May 3, 2026, 3:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6bb7a0ae08190813411fa677430aa |
completed | May 3, 2026, 3:05 a.m. |
Created at: April 9, 2026, 5:38 p.m.