Triple
T6957736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | High Rhine |
E161287
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Bad Säckingen
Bad Säckingen is a historic spa town in southwestern Germany on the Rhine River, known for its medieval old town and one of the longest covered wooden bridges in Europe.
|
E645232
|
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: Bad Säckingen | Statement: [High Rhine, hasPart, Bad Säckingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Säckingen Context triple: [High Rhine, hasPart, Bad Säckingen]
-
A.
Bad Cannstatt
Bad Cannstatt is a historic district of Stuttgart, Germany, known for its mineral springs, traditional architecture, and the Cannstatter Volksfest beer festival.
-
B.
Schaafheim
Schaafheim is a municipality in the state of Hesse in central Germany.
-
C.
Bad Wurzach
Bad Wurzach is a spa town in the Allgäu region of southern Germany, known for its moorland landscapes and therapeutic mud baths.
-
D.
Bad Schwalbach
Bad Schwalbach is a spa town in the German state of Hesse, known for its mineral springs and location in the Taunus mountains.
-
E.
Hausach
Hausach is a small town in Germany’s Black Forest region, known for its scenic valley setting along the Kinzig River and its traditional timber-framed 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: Bad Säckingen Triple: [High Rhine, hasPart, Bad Säckingen]
Generated description
Bad Säckingen is a historic spa town in southwestern Germany on the Rhine River, known for its medieval old town and one of the longest covered wooden bridges in Europe.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bad Säckingen Target entity description: Bad Säckingen is a historic spa town in southwestern Germany on the Rhine River, known for its medieval old town and one of the longest covered wooden bridges in Europe.
-
A.
Bad Cannstatt
Bad Cannstatt is a historic district of Stuttgart, Germany, known for its mineral springs, traditional architecture, and the Cannstatter Volksfest beer festival.
-
B.
Schaafheim
Schaafheim is a municipality in the state of Hesse in central Germany.
-
C.
Bad Wurzach
Bad Wurzach is a spa town in the Allgäu region of southern Germany, known for its moorland landscapes and therapeutic mud baths.
-
D.
Bad Schwalbach
Bad Schwalbach is a spa town in the German state of Hesse, known for its mineral springs and location in the Taunus mountains.
-
E.
Hausach
Hausach is a small town in Germany’s Black Forest region, known for its scenic valley setting along the Kinzig River and its traditional timber-framed 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_69c68852a9a0819097797e31d492e273 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dad0e52081908b524dc6a66bab01 |
completed | March 27, 2026, 7:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7ad70f09881909f1a03f295486942 |
completed | March 28, 2026, 10:29 a.m. |
| NEDg | Description generation | batch_69c7ade26e24819085f431a576d29712 |
completed | March 28, 2026, 10:30 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c7ae9f8f648190adc5cdf08bc01d93 |
completed | March 28, 2026, 10:34 a.m. |
Created at: March 27, 2026, 2:29 p.m.