Triple
T2963058
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erlangen |
E80092
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object |
Eltersdorf
Eltersdorf is a district of the Bavarian city of Erlangen in Germany, known for its residential character and local transport connections.
|
E316841
|
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: Eltersdorf | Statement: [Erlangen, hasDistrict, Eltersdorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eltersdorf Context triple: [Erlangen, hasDistrict, Eltersdorf]
-
A.
Burkhardtsdorf
Burkhardtsdorf is a small municipality in the Erzgebirge (Ore Mountains) region of Saxony, eastern Germany.
-
B.
Hubersdorf
Hubersdorf is a small municipality located in the canton of Solothurn in northwestern Switzerland.
-
C.
Hermsdorf
Hermsdorf is a residential locality in the Berlin borough of Reinickendorf, known for its green surroundings and village-like character on the city’s northern edge.
-
D.
Hägendorf
Hägendorf is a municipality in the canton of Solothurn in northwestern Switzerland, known for its residential character and proximity to the Jura mountains.
-
E.
Hennigsdorf
Hennigsdorf is a town in the German state of Brandenburg, located just northwest of Berlin and known for its industrial heritage and proximity to the Havel River.
- 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: Eltersdorf Triple: [Erlangen, hasDistrict, Eltersdorf]
Generated description
Eltersdorf is a district of the Bavarian city of Erlangen in Germany, known for its residential character and local transport connections.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Eltersdorf Target entity description: Eltersdorf is a district of the Bavarian city of Erlangen in Germany, known for its residential character and local transport connections.
-
A.
Burkhardtsdorf
Burkhardtsdorf is a small municipality in the Erzgebirge (Ore Mountains) region of Saxony, eastern Germany.
-
B.
Hubersdorf
Hubersdorf is a small municipality located in the canton of Solothurn in northwestern Switzerland.
-
C.
Hermsdorf
Hermsdorf is a residential locality in the Berlin borough of Reinickendorf, known for its green surroundings and village-like character on the city’s northern edge.
-
D.
Hägendorf
Hägendorf is a municipality in the canton of Solothurn in northwestern Switzerland, known for its residential character and proximity to the Jura mountains.
-
E.
Hennigsdorf
Hennigsdorf is a town in the German state of Brandenburg, located just northwest of Berlin and known for its industrial heritage and proximity to the Havel River.
- 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_69ad8b1341848190bd19dbf46892887d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9957602c819089b673966fd619e0 |
completed | March 8, 2026, 3:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b108e14e288190bcca59b2d8132996 |
completed | March 11, 2026, 6:17 a.m. |
| NEDg | Description generation | batch_69b10e65ae8c81908f4f9ba9dd4de206 |
completed | March 11, 2026, 6:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b10ece90e88190b84dde41579bf751 |
completed | March 11, 2026, 6:42 a.m. |
Created at: March 8, 2026, 2:57 p.m.