Triple
T9010333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Landkreis Günzburg |
E215451
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Gundremmingen
Gundremmingen is a small Bavarian municipality best known for hosting one of Germany’s major nuclear power plants.
|
E777047
|
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: Gundremmingen | Statement: [Landkreis Günzburg, contains, Gundremmingen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gundremmingen Context triple: [Landkreis Günzburg, contains, Gundremmingen]
-
A.
Reichenau
Reichenau is a German municipality best known for its UNESCO-listed monastic island on Lake Constance, renowned for its medieval abbey and cultural heritage.
-
B.
Bad Schussenried
Bad Schussenried is a spa town in southern Germany known for its historic monastery complex and scenic location in Upper Swabia.
-
C.
Bad Reichenhall
Bad Reichenhall is a Bavarian spa town in southeastern Germany, renowned for its alpine setting and historic salt production.
-
D.
Adendorf
Adendorf is a village-sized district within the municipality of Wachtberg in the Rhein-Sieg-Kreis region of North Rhine-Westphalia, Germany.
-
E.
Drensteinfurt
Drensteinfurt is a small town in North Rhine-Westphalia, Germany, known for its historic architecture and location in the Münsterland 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: Gundremmingen Triple: [Landkreis Günzburg, contains, Gundremmingen]
Generated description
Gundremmingen is a small Bavarian municipality best known for hosting one of Germany’s major nuclear power plants.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gundremmingen Target entity description: Gundremmingen is a small Bavarian municipality best known for hosting one of Germany’s major nuclear power plants.
-
A.
Reichenau
Reichenau is a German municipality best known for its UNESCO-listed monastic island on Lake Constance, renowned for its medieval abbey and cultural heritage.
-
B.
Bad Schussenried
Bad Schussenried is a spa town in southern Germany known for its historic monastery complex and scenic location in Upper Swabia.
-
C.
Bad Reichenhall
Bad Reichenhall is a Bavarian spa town in southeastern Germany, renowned for its alpine setting and historic salt production.
-
D.
Adendorf
Adendorf is a village-sized district within the municipality of Wachtberg in the Rhein-Sieg-Kreis region of North Rhine-Westphalia, Germany.
-
E.
Drensteinfurt
Drensteinfurt is a small town in North Rhine-Westphalia, Germany, known for its historic architecture and location in the Münsterland 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_69ca83a2bf088190986ee7a8eb90407d |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc69c1571881908d0b144786b5ee1f |
completed | April 1, 2026, 12:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d017643df48190abdb672a3f85aef3 |
completed | April 3, 2026, 7:39 p.m. |
| NEDg | Description generation | batch_69d019059e8481909a696575366aa0b6 |
completed | April 3, 2026, 7:46 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d019a2736c8190880c8f3786cf353b |
completed | April 3, 2026, 7:48 p.m. |
Created at: March 30, 2026, 7:06 p.m.