Triple
T17927099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amberg-Sulzbach |
E448225
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Vilseck
Vilseck is a small Bavarian town in Germany known for its historic medieval center and proximity to the U.S. Army Garrison Bavaria (Rose Barracks).
|
E1296886
|
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: Vilseck | Statement: [Amberg-Sulzbach, contains, Vilseck]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vilseck Context triple: [Amberg-Sulzbach, contains, Vilseck]
-
A.
Grafenwöhr
Grafenwöhr is a Bavarian town best known for hosting one of the largest U.S. Army training areas in Europe.
-
B.
Wechmar
Wechmar is a village in Thuringia, Germany, historically notable as the ancestral home of the Bach musical family.
-
C.
Vockenhausen
Vockenhausen is a district of the town of Eppstein in the German state of Hesse.
-
D.
Hohenfels
Hohenfels is a prominent peak in Namibia’s Erongo Mountains, known as the range’s highest summit.
-
E.
Gaildorf
Gaildorf is a small town in the German state of Baden-Württemberg, situated in the Kocher River valley and known for its historic center and surrounding rural landscape.
- 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: Vilseck Triple: [Amberg-Sulzbach, contains, Vilseck]
Generated description
Vilseck is a small Bavarian town in Germany known for its historic medieval center and proximity to the U.S. Army Garrison Bavaria (Rose Barracks).
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vilseck Target entity description: Vilseck is a small Bavarian town in Germany known for its historic medieval center and proximity to the U.S. Army Garrison Bavaria (Rose Barracks).
-
A.
Grafenwöhr
Grafenwöhr is a Bavarian town best known for hosting one of the largest U.S. Army training areas in Europe.
-
B.
Wechmar
Wechmar is a village in Thuringia, Germany, historically notable as the ancestral home of the Bach musical family.
-
C.
Vockenhausen
Vockenhausen is a district of the town of Eppstein in the German state of Hesse.
-
D.
Hohenfels
Hohenfels is a prominent peak in Namibia’s Erongo Mountains, known as the range’s highest summit.
-
E.
Gaildorf
Gaildorf is a small town in the German state of Baden-Württemberg, situated in the Kocher River valley and known for its historic center and surrounding rural landscape.
- 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_69d8b9f79d14819095540856928f0e25 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4a54f29a88190b035d8473765bde5 |
completed | April 19, 2026, 9:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a032136546c81909556406f96f8c04b |
completed | May 12, 2026, 12:46 p.m. |
| NEDg | Description generation | batch_6a032282ce908190a484708ec9995d60 |
completed | May 12, 2026, 12:52 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a032466070c819084e17d0bf5612844 |
completed | May 12, 2026, 1 p.m. |
Created at: April 10, 2026, 10:20 a.m.