Triple
T8962281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Landkreis Sigmaringen |
E214035
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
town of Mengen
The town of Mengen is a small municipality in the district of Sigmaringen in the German state of Baden-Württemberg, known for its historic center and location near the upper Danube.
|
E769126
|
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: town of Mengen | Statement: [Landkreis Sigmaringen, contains, town of Mengen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: town of Mengen Context triple: [Landkreis Sigmaringen, contains, town of Mengen]
-
A.
Mundenheim
Mundenheim is a district of the industrial city of Ludwigshafen am Rhein in the German state of Rhineland-Palatinate.
-
B.
Mellingen
Mellingen is a small Swiss town in the canton of Aargau known for its historic old town and riverside setting along the Reuss.
-
C.
Merklingen
Merklingen is a small town in the Alb-Donau district of the German state of Baden-Württemberg.
-
D.
Merklingen
Merklingen is a village and district of the town of Weil der Stadt in the German state of Baden-Württemberg.
-
E.
Menzingen
Menzingen is a municipality in the canton of Zug in central Switzerland, known for its rural landscape and location in the pre-Alpine region.
- 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: town of Mengen Triple: [Landkreis Sigmaringen, contains, town of Mengen]
Generated description
The town of Mengen is a small municipality in the district of Sigmaringen in the German state of Baden-Württemberg, known for its historic center and location near the upper Danube.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: town of Mengen Target entity description: The town of Mengen is a small municipality in the district of Sigmaringen in the German state of Baden-Württemberg, known for its historic center and location near the upper Danube.
-
A.
Mundenheim
Mundenheim is a district of the industrial city of Ludwigshafen am Rhein in the German state of Rhineland-Palatinate.
-
B.
Mellingen
Mellingen is a small Swiss town in the canton of Aargau known for its historic old town and riverside setting along the Reuss.
-
C.
Merklingen
Merklingen is a small town in the Alb-Donau district of the German state of Baden-Württemberg.
-
D.
Merklingen
Merklingen is a village and district of the town of Weil der Stadt in the German state of Baden-Württemberg.
-
E.
Menzingen
Menzingen is a municipality in the canton of Zug in central Switzerland, known for its rural landscape and location in the pre-Alpine region.
- 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_69ca839cd6008190a1546a701a56710c |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6749e5008190a01f42a2e772dd54 |
completed | April 1, 2026, 12:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc94e088881909506b229d1fff44a |
completed | April 3, 2026, 2:06 p.m. |
| NEDg | Description generation | batch_69cfc9ef5e548190a134c2bf0aa380b5 |
completed | April 3, 2026, 2:08 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cfca735c50819088c2805c62d96d62 |
completed | April 3, 2026, 2:10 p.m. |
Created at: March 30, 2026, 7:01 p.m.