Triple
T18213713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fraubrunnen |
E436096
|
entity |
| Predicate | neighboringMunicipality |
P17964
|
FINISHED |
| Object |
Münchringen
Münchringen is a small village and former municipality in the canton of Bern, Switzerland.
|
E1341150
|
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: Münchringen | Statement: [Fraubrunnen, neighboringMunicipality, Münchringen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Münchringen Context triple: [Fraubrunnen, neighboringMunicipality, Münchringen]
-
A.
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.
-
B.
Korntal-Münchingen
Korntal-Münchingen is a small town in the German state of Baden-Württemberg, near Stuttgart, known for its residential character and local industry.
-
C.
Vaihingen
Vaihingen is a district in the southwest of Stuttgart, Germany, known for its mix of residential areas, business parks, and proximity to major transport links.
-
D.
Schwabmünchen
Schwabmünchen is a small Bavarian town in southern Germany known for its historic center and location near the city of Augsburg.
-
E.
Wehringen
Wehringen is a small municipality in Bavaria, Germany, situated in the region surrounding the city of Augsburg.
- 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: Münchringen Triple: [Fraubrunnen, neighboringMunicipality, Münchringen]
Generated description
Münchringen is a small village and former municipality in the canton of Bern, Switzerland.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Münchringen Target entity description: Münchringen is a small village and former municipality in the canton of Bern, Switzerland.
-
A.
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.
-
B.
Korntal-Münchingen
Korntal-Münchingen is a small town in the German state of Baden-Württemberg, near Stuttgart, known for its residential character and local industry.
-
C.
Vaihingen
Vaihingen is a district in the southwest of Stuttgart, Germany, known for its mix of residential areas, business parks, and proximity to major transport links.
-
D.
Schwabmünchen
Schwabmünchen is a small Bavarian town in southern Germany known for its historic center and location near the city of Augsburg.
-
E.
Wehringen
Wehringen is a small municipality in Bavaria, Germany, situated in the region surrounding the city of Augsburg.
- 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_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e475953c81909f792793ded2057e |
completed | April 19, 2026, 2:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a053d1f3ebc8190bb62be8ffb7c0b64 |
completed | May 14, 2026, 3:10 a.m. |
| NEDg | Description generation | batch_6a053dd2222c819095099ba116b1c0a7 |
completed | May 14, 2026, 3:13 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a053e4b79d081908bddd0608ec1b519 |
completed | May 14, 2026, 3:15 a.m. |
Created at: April 10, 2026, 10:32 a.m.