Triple
T228684
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lower Saxony |
E4364
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Salzgitter
Salzgitter is a major industrial city in central Germany known for its steel production and location within the federal state of Lower Saxony.
|
E75169
|
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: Salzgitter | Statement: [Lower Saxony, containsCity, Salzgitter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Salzgitter Context triple: [Lower Saxony, containsCity, Salzgitter]
-
A.
Lünen
Lünen is a town in North Rhine-Westphalia, Germany, known as an industrial and commuter city in the Ruhr area.
-
B.
Kaiserslautern
Kaiserslautern is a city in southwestern Germany known for its historic old town, technical university, and prominent football club 1. FC Kaiserslautern.
-
C.
Lüneburg
Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
-
D.
Wolfsburg
Wolfsburg is a German city best known as the headquarters and main production site of the Volkswagen automobile company.
-
E.
Duisburg
Duisburg is a major industrial and port city in western Germany’s Ruhr region, known for its steel production and one of the world’s largest inland harbors.
- 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: Salzgitter Triple: [Lower Saxony, containsCity, Salzgitter]
Generated description
Salzgitter is a major industrial city in central Germany known for its steel production and location within the federal state of Lower Saxony.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Salzgitter Target entity description: Salzgitter is a major industrial city in central Germany known for its steel production and location within the federal state of Lower Saxony.
-
A.
Lünen
Lünen is a town in North Rhine-Westphalia, Germany, known as an industrial and commuter city in the Ruhr area.
-
B.
Kaiserslautern
Kaiserslautern is a city in southwestern Germany known for its historic old town, technical university, and prominent football club 1. FC Kaiserslautern.
-
C.
Lüneburg
Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
-
D.
Wolfsburg
Wolfsburg is a German city best known as the headquarters and main production site of the Volkswagen automobile company.
-
E.
Duisburg
Duisburg is a major industrial and port city in western Germany’s Ruhr region, known for its steel production and one of the world’s largest inland harbors.
- 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_69a257363ffc81909757bde7ab3404da |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25c9140c48190b90647400854b37e |
completed | Feb. 28, 2026, 3:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a5216a74088190bf8d363d32952c28 |
completed | March 2, 2026, 5:34 a.m. |
| NEDg | Description generation | batch_69a521ce19e08190aadeb913977c2d2e |
completed | March 2, 2026, 5:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a5226144f881908e0d1add6be6e156 |
completed | March 2, 2026, 5:38 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.