Triple
T2817477
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dresden–Werdau railway |
E54323
|
entity |
| Predicate | terminus |
P388
|
FINISHED |
| Object |
Werdau
Werdau is a town in the Free State of Saxony in eastern Germany, historically known for its textile and engineering industries.
|
E432350
|
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: Werdau | Statement: [Dresden–Werdau railway, terminus, Werdau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Werdau Context triple: [Dresden–Werdau railway, terminus, Werdau]
-
A.
Wurzen
Wurzen is a historic town in the German state of Saxony, known for its medieval architecture and location on the river Mulde east of Leipzig.
-
B.
Suhl
Suhl is a city in central Germany known historically as a center of firearms manufacturing and located in the federal state of Thuringia.
-
C.
Deggendorf
Deggendorf is a town in southeastern Germany situated on the Danube River, known as a regional commercial and transportation hub near the Bavarian Forest.
-
D.
Zwickau
Zwickau is a city in the German state of Saxony known historically as an important center of the automotive industry and as the birthplace of composer Robert Schumann.
-
E.
Lankwitz
Lankwitz is a residential locality in the southwestern part of Berlin, known for its quiet neighborhoods, green spaces, and mix of historic and modern architecture.
- 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: Werdau Triple: [Dresden–Werdau railway, terminus, Werdau]
Generated description
Werdau is a town in the Free State of Saxony in eastern Germany, historically known for its textile and engineering industries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Werdau Target entity description: Werdau is a town in the Free State of Saxony in eastern Germany, historically known for its textile and engineering industries.
-
A.
Wurzen
Wurzen is a historic town in the German state of Saxony, known for its medieval architecture and location on the river Mulde east of Leipzig.
-
B.
Suhl
Suhl is a city in central Germany known historically as a center of firearms manufacturing and located in the federal state of Thuringia.
-
C.
Deggendorf
Deggendorf is a town in southeastern Germany situated on the Danube River, known as a regional commercial and transportation hub near the Bavarian Forest.
-
D.
Zwickau
Zwickau is a city in the German state of Saxony known historically as an important center of the automotive industry and as the birthplace of composer Robert Schumann.
-
E.
Lankwitz
Lankwitz is a residential locality in the southwestern part of Berlin, known for its quiet neighborhoods, green spaces, and mix of historic and modern architecture.
- 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_69ab49de0af08190b3da69683be1e728 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abde500d3c8190b435a20a0f9d3b9d |
completed | March 7, 2026, 8:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5db52bcfc8190857a3ea5157d8416 |
completed | March 14, 2026, 10:04 p.m. |
| NEDg | Description generation | batch_69b5dbe844e4819099dbd1ed65f262fb |
completed | March 14, 2026, 10:06 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5dc5a2b008190907150ada5714fac |
completed | March 14, 2026, 10:08 p.m. |
Created at: March 6, 2026, 9:59 p.m.