Triple
T8020375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unstrut River region |
E186724
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Straußfurt
Straußfurt is a municipality in the German state of Thuringia, known for its rural setting and proximity to the Unstrut River.
|
E760304
|
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: Straußfurt | Statement: [Unstrut River region, contains, Straußfurt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Straußfurt Context triple: [Unstrut River region, contains, Straußfurt]
-
A.
Wiesbaden
Wiesbaden is a historic spa city in western Germany known for its thermal springs, elegant architecture, and role as a regional administrative and cultural center.
-
B.
Wetzlar
Wetzlar is a historic German city in the state of Hesse, known for its medieval old town and its long tradition in optics and precision engineering.
-
C.
Staßfurt
Staßfurt is a town in Saxony-Anhalt, Germany, historically known for its salt mining and chemical industry.
-
D.
Kassel
Kassel is a city in central Germany known for its cultural institutions and as the host of the renowned contemporary art exhibition documenta.
-
E.
Schweinfurt
Schweinfurt is a city in northern Bavaria, Germany, historically known for its ball bearing industry and as a strategic target during World War II.
- 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: Straußfurt Triple: [Unstrut River region, contains, Straußfurt]
Generated description
Straußfurt is a municipality in the German state of Thuringia, known for its rural setting and proximity to the Unstrut River.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Straußfurt Target entity description: Straußfurt is a municipality in the German state of Thuringia, known for its rural setting and proximity to the Unstrut River.
-
A.
Wiesbaden
Wiesbaden is a historic spa city in western Germany known for its thermal springs, elegant architecture, and role as a regional administrative and cultural center.
-
B.
Wetzlar
Wetzlar is a historic German city in the state of Hesse, known for its medieval old town and its long tradition in optics and precision engineering.
-
C.
Staßfurt
Staßfurt is a town in Saxony-Anhalt, Germany, historically known for its salt mining and chemical industry.
-
D.
Kassel
Kassel is a city in central Germany known for its cultural institutions and as the host of the renowned contemporary art exhibition documenta.
-
E.
Schweinfurt
Schweinfurt is a city in northern Bavaria, Germany, historically known for its ball bearing industry and as a strategic target during World War II.
- 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_69ca82ac7fc081909b1398cf025423af |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3e8d90488190b57d1e748e272061 |
completed | March 31, 2026, 3:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf883201d481909efd2f57e852a175 |
completed | April 3, 2026, 9:28 a.m. |
| NEDg | Description generation | batch_69cf8a3d8e548190911d44ee36875d44 |
completed | April 3, 2026, 9:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cf8ae86e1881908a77f660c061bf69 |
completed | April 3, 2026, 9:39 a.m. |
Created at: March 30, 2026, 5:20 p.m.