Triple
T3927740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bern metropolitan area |
E93317
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Münsingen
Münsingen is a Swiss municipality in the canton of Bern, known for its scenic location in the Aare valley between Bern and Thun.
|
E427362
|
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: Münsingen | Statement: [Bern metropolitan area, contains, Münsingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Münsingen Context triple: [Bern metropolitan area, contains, Münsingen]
-
A.
Memmingen
Memmingen is a historic town in the Bavarian region of Germany, known for its well-preserved medieval old town and role as a regional transport hub.
-
B.
Miesbach
Miesbach is a historic town in southern Germany known for its traditional Bavarian culture and picturesque Alpine foothill setting.
-
C.
Münklingen
Münklingen is a village and district of the town Weil der Stadt in the German state of Baden-Württemberg.
-
D.
Möhringen
Möhringen is a district of Stuttgart in the German state of Baden-Württemberg, known as a residential area that also hosts U.S. military facilities.
-
E.
Gernsbach
Gernsbach is a historic town in southwestern Germany’s Black Forest region, known for its medieval old town and picturesque setting along the Murg River.
- 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: Münsingen Triple: [Bern metropolitan area, contains, Münsingen]
Generated description
Münsingen is a Swiss municipality in the canton of Bern, known for its scenic location in the Aare valley between Bern and Thun.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Münsingen Target entity description: Münsingen is a Swiss municipality in the canton of Bern, known for its scenic location in the Aare valley between Bern and Thun.
-
A.
Memmingen
Memmingen is a historic town in the Bavarian region of Germany, known for its well-preserved medieval old town and role as a regional transport hub.
-
B.
Miesbach
Miesbach is a historic town in southern Germany known for its traditional Bavarian culture and picturesque Alpine foothill setting.
-
C.
Münklingen
Münklingen is a village and district of the town Weil der Stadt in the German state of Baden-Württemberg.
-
D.
Möhringen
Möhringen is a district of Stuttgart in the German state of Baden-Württemberg, known as a residential area that also hosts U.S. military facilities.
-
E.
Gernsbach
Gernsbach is a historic town in southwestern Germany’s Black Forest region, known for its medieval old town and picturesque setting along the Murg River.
- 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_69aed96bfa1081908f7b30f2c647dee6 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeeda4f9d481908dda1b5a826ab64d |
completed | March 9, 2026, 3:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5b74b0cd08190bebb483a72d0b59a |
completed | March 14, 2026, 7:30 p.m. |
| NEDg | Description generation | batch_69b5b83759c08190b080565bb1ac8bb4 |
completed | March 14, 2026, 7:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5b8be90c88190a4852c625e326f6b |
completed | March 14, 2026, 7:36 p.m. |
Created at: March 9, 2026, 3:23 p.m.