Triple
T2049906
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schleswig-Holstein |
E45540
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Neumünster
Neumünster is a mid-sized industrial and commercial city in northern Germany known for its textile history and central location within Schleswig-Holstein.
|
E248689
|
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: Neumünster | Statement: [Schleswig-Holstein, hasCity, Neumünster]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Neumünster Context triple: [Schleswig-Holstein, hasCity, Neumünster]
-
A.
Lüneburg
Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
-
B.
Delmenhorst
Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
-
C.
Nienburg
Nienburg is a historic town in Lower Saxony, Germany, known for its medieval architecture and scenic location along the Weser River.
-
D.
Neustadt
Neustadt is a vibrant district of Dresden, Germany, known for its historic architecture, lively arts scene, and numerous bars, cafes, and cultural venues.
-
E.
Neustadt
Neustadt is a district of the Austrian city of Salzburg, known for its central urban character within the historic and cultural landscape of the city.
- 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: Neumünster Triple: [Schleswig-Holstein, hasCity, Neumünster]
Generated description
Neumünster is a mid-sized industrial and commercial city in northern Germany known for its textile history and central location within Schleswig-Holstein.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Neumünster Target entity description: Neumünster is a mid-sized industrial and commercial city in northern Germany known for its textile history and central location within Schleswig-Holstein.
-
A.
Lüneburg
Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
-
B.
Delmenhorst
Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
-
C.
Nienburg
Nienburg is a historic town in Lower Saxony, Germany, known for its medieval architecture and scenic location along the Weser River.
-
D.
Neustadt
Neustadt is a vibrant district of Dresden, Germany, known for its historic architecture, lively arts scene, and numerous bars, cafes, and cultural venues.
-
E.
Neustadt
Neustadt is a district of the Austrian city of Salzburg, known for its central urban character within the historic and cultural landscape of the city.
- 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_69a8891948208190ab7898da21824c77 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb98e10d48190bb96cd1f8ea3c08b |
completed | March 7, 2026, 5:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae6ae88ad08190a0a638cad2566f2e |
completed | March 9, 2026, 6:38 a.m. |
| NEDg | Description generation | batch_69ae6b9da51c819085beb79a14f5d8b5 |
completed | March 9, 2026, 6:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae6c2a465c8190a9fe2a465e9ac3f0 |
completed | March 9, 2026, 6:43 a.m. |
Created at: March 4, 2026, 7:39 p.m.