Triple
T10442106
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hof district |
E246193
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Bad Steben
Bad Steben is a spa town in northern Bavaria, Germany, renowned for its thermal baths and health resorts.
|
E863172
|
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 Steben | Statement: [Hof district, contains, Bad Steben]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Steben Context triple: [Hof district, contains, Bad Steben]
-
A.
Stühlingen
Stühlingen is a small town in the state of Baden-Württemberg in southwestern Germany, near the Swiss border, known for its scenic setting in the Black Forest region.
-
B.
Erstfeld
Erstfeld is a municipality in the Swiss canton of Uri, situated in a mountainous valley that serves as an important transport corridor through the Alps.
-
C.
Hademstorf
Hademstorf is a small municipality in Lower Saxony, Germany, situated in the Heidekreis district.
-
D.
Burgstädt
Burgstädt is a small town in the German state of Saxony, known for its traditional architecture and location near the city of Chemnitz.
-
E.
Trostberg
Trostberg is a small Bavarian town in southeastern Germany known for its historic old town and chemical industry.
- 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 Steben Triple: [Hof district, contains, Bad Steben]
Generated description
Bad Steben is a spa town in northern Bavaria, Germany, renowned for its thermal baths and health resorts.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bad Steben Target entity description: Bad Steben is a spa town in northern Bavaria, Germany, renowned for its thermal baths and health resorts.
-
A.
Stühlingen
Stühlingen is a small town in the state of Baden-Württemberg in southwestern Germany, near the Swiss border, known for its scenic setting in the Black Forest region.
-
B.
Erstfeld
Erstfeld is a municipality in the Swiss canton of Uri, situated in a mountainous valley that serves as an important transport corridor through the Alps.
-
C.
Hademstorf
Hademstorf is a small municipality in Lower Saxony, Germany, situated in the Heidekreis district.
-
D.
Burgstädt
Burgstädt is a small town in the German state of Saxony, known for its traditional architecture and location near the city of Chemnitz.
-
E.
Trostberg
Trostberg is a small Bavarian town in southeastern Germany known for its historic old town and chemical industry.
- 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_69d381c04fe08190957c26c526a3b05a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4fb9ebf488190ae776bd65e94cb00 |
completed | April 7, 2026, 12:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d87ed6edd88190afd5063daba58a46 |
completed | April 10, 2026, 4:38 a.m. |
| NEDg | Description generation | batch_69d8837f98e08190bbffa535f94daf48 |
completed | April 10, 2026, 4:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d889d0e70c8190953d164f34f01e47 |
completed | April 10, 2026, 5:25 a.m. |
Created at: April 6, 2026, 12:15 p.m.