Triple
T13110540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greiz |
E310957
|
entity |
| Predicate | hasNeighbouringMunicipality |
P224
|
FINISHED |
| Object |
Langenwetzendorf
Langenwetzendorf is a rural municipality in the district of Greiz in the eastern German state of Thuringia.
|
E1023069
|
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: Langenwetzendorf | Statement: [Greiz, hasNeighbouringMunicipality, Langenwetzendorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Langenwetzendorf Context triple: [Greiz, hasNeighbouringMunicipality, Langenwetzendorf]
-
A.
Aulendorf
Aulendorf is a small town in the Upper Swabia region of southern Germany, known for its historic castle and spa facilities.
-
B.
Burkhardtsdorf
Burkhardtsdorf is a small municipality in the Erzgebirge (Ore Mountains) region of Saxony, eastern Germany.
-
C.
Hermsdorf
Hermsdorf is a small town in the German state of Thuringia, known as an industrial and transport hub near the city of Jena.
-
D.
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.
-
E.
Heinersdorf
Heinersdorf is a residential locality in the borough of Pankow in Berlin, Germany, known for its suburban character and proximity to the city center.
- 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: Langenwetzendorf Triple: [Greiz, hasNeighbouringMunicipality, Langenwetzendorf]
Generated description
Langenwetzendorf is a rural municipality in the district of Greiz in the eastern German state of Thuringia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Langenwetzendorf Target entity description: Langenwetzendorf is a rural municipality in the district of Greiz in the eastern German state of Thuringia.
-
A.
Aulendorf
Aulendorf is a small town in the Upper Swabia region of southern Germany, known for its historic castle and spa facilities.
-
B.
Burkhardtsdorf
Burkhardtsdorf is a small municipality in the Erzgebirge (Ore Mountains) region of Saxony, eastern Germany.
-
C.
Hermsdorf
Hermsdorf is a small town in the German state of Thuringia, known as an industrial and transport hub near the city of Jena.
-
D.
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.
-
E.
Heinersdorf
Heinersdorf is a residential locality in the borough of Pankow in Berlin, Germany, known for its suburban character and proximity to the city center.
- 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_69d806a872d08190a329806f8ff30df4 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d9817e4f408190b77c198b4157d77a |
completed | April 10, 2026, 11:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6e27d8110819087ade3537f867ae0 |
completed | May 3, 2026, 5:51 a.m. |
| NEDg | Description generation | batch_69f6e4c5e2888190b0bfcdf2cc25ad5f |
completed | May 3, 2026, 6:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6e5979df881909db42a735b9b1064 |
completed | May 3, 2026, 6:05 a.m. |
Created at: April 9, 2026, 9:05 p.m.