Triple
T2938239
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oker |
E79320
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object |
Vienenburg
Vienenburg is a district of Goslar in Lower Saxony, Germany, known for its historic town center and proximity to the Harz Mountains.
|
E312129
|
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: Vienenburg | Statement: [Oker, passesNear, Vienenburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vienenburg Context triple: [Oker, passesNear, Vienenburg]
-
A.
Günzburg
Günzburg is a small Bavarian town in southern Germany, historically notable as the birthplace of Nazi physician Josef Mengele.
-
B.
Blaubeuren
Blaubeuren is a historic town in the Alb-Donau district of Baden-Württemberg, Germany, known for its medieval old town and the karst spring Blautopf.
-
C.
Lichtenfels
Lichtenfels is a town in the Upper Franconia region of Bavaria, Germany, known for its basket-making tradition and historic architecture.
-
D.
Offenburg
Offenburg is a city in southwestern Germany’s Baden-Württemberg state, known as a regional economic and transport hub near the French border in the Upper Rhine region.
-
E.
Markranstädt
Markranstädt is a small town in the German state of Saxony, located near Leipzig and known for its local industry and proximity to the Kulkwitzer See recreation area.
- 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: Vienenburg Triple: [Oker, passesNear, Vienenburg]
Generated description
Vienenburg is a district of Goslar in Lower Saxony, Germany, known for its historic town center and proximity to the Harz Mountains.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vienenburg Target entity description: Vienenburg is a district of Goslar in Lower Saxony, Germany, known for its historic town center and proximity to the Harz Mountains.
-
A.
Günzburg
Günzburg is a small Bavarian town in southern Germany, historically notable as the birthplace of Nazi physician Josef Mengele.
-
B.
Blaubeuren
Blaubeuren is a historic town in the Alb-Donau district of Baden-Württemberg, Germany, known for its medieval old town and the karst spring Blautopf.
-
C.
Lichtenfels
Lichtenfels is a town in the Upper Franconia region of Bavaria, Germany, known for its basket-making tradition and historic architecture.
-
D.
Offenburg
Offenburg is a city in southwestern Germany’s Baden-Württemberg state, known as a regional economic and transport hub near the French border in the Upper Rhine region.
-
E.
Markranstädt
Markranstädt is a small town in the German state of Saxony, located near Leipzig and known for its local industry and proximity to the Kulkwitzer See recreation area.
- 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_69ad8b0fbab081908f6a61567c045d8d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad986c1c0c8190a6a9f17082438cfd |
completed | March 8, 2026, 3:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b086837ddc8190af27c7facd629691 |
completed | March 10, 2026, 9 p.m. |
| NEDg | Description generation | batch_69b0d15ec81c81909c22e265dd0263df |
completed | March 11, 2026, 2:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b0d524994c8190b8b6ff05eee8696a |
completed | March 11, 2026, 2:36 a.m. |
Created at: March 8, 2026, 2:56 p.m.