Triple
T8797475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bad Camberg |
E209324
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Oberselters
Oberselters is a district of the spa town Bad Camberg in Hesse, Germany, known for its rural character within the Taunus region.
|
E803690
|
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: Oberselters | Statement: [Bad Camberg, hasPart, Oberselters]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oberselters Context triple: [Bad Camberg, hasPart, Oberselters]
-
A.
Vellinghausen
Vellinghausen is a village in western Germany known historically as the site of the Battle of Vellinghausen during the Seven Years' War.
-
B.
Ochsenhausen
Ochsenhausen is a small historic town in the German state of Baden-Württemberg, best known for its former Benedictine monastery, Ochsenhausen Abbey.
-
C.
Hersbruck
Hersbruck is a small historic town in the Franconian region of Bavaria, Germany, known for its picturesque setting in the Pegnitz Valley and traditional Bavarian architecture.
-
D.
Lülsfeld
Lülsfeld is a small municipality in the Schweinfurt district of Lower Franconia in northern Bavaria, Germany.
-
E.
Albershausen
Albershausen is a small municipality in the German state of Baden-Württemberg, located in the Göppingen district in southern Germany.
- 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: Oberselters Triple: [Bad Camberg, hasPart, Oberselters]
Generated description
Oberselters is a district of the spa town Bad Camberg in Hesse, Germany, known for its rural character within the Taunus region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Oberselters Target entity description: Oberselters is a district of the spa town Bad Camberg in Hesse, Germany, known for its rural character within the Taunus region.
-
A.
Vellinghausen
Vellinghausen is a village in western Germany known historically as the site of the Battle of Vellinghausen during the Seven Years' War.
-
B.
Ochsenhausen
Ochsenhausen is a small historic town in the German state of Baden-Württemberg, best known for its former Benedictine monastery, Ochsenhausen Abbey.
-
C.
Hersbruck
Hersbruck is a small historic town in the Franconian region of Bavaria, Germany, known for its picturesque setting in the Pegnitz Valley and traditional Bavarian architecture.
-
D.
Lülsfeld
Lülsfeld is a small municipality in the Schweinfurt district of Lower Franconia in northern Bavaria, Germany.
-
E.
Albershausen
Albershausen is a small municipality in the German state of Baden-Württemberg, located in the Göppingen district in southern Germany.
- 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_69ca836240888190a62b262e56a69d2f |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5fa370d08190885ef65e3a3e56d3 |
completed | March 31, 2026, 11:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d139a069f881909cc59bad0f110830 |
completed | April 4, 2026, 4:17 p.m. |
| NEDg | Description generation | batch_69d13bb4e3688190aaf6a0ba117296ae |
completed | April 4, 2026, 4:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d13c1b8fd481909dc4a98d64301d2f |
completed | April 4, 2026, 4:28 p.m. |
Created at: March 30, 2026, 6:44 p.m.