Triple
T990097
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hermann Weyl |
E21368
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object |
Elmshorn
Elmshorn is a town in northern Germany’s Schleswig-Holstein state, known as an industrial and commuter hub northwest of Hamburg.
|
E155525
|
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: Elmshorn | Statement: [Hermann Weyl, placeOfBirth, Elmshorn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elmshorn Context triple: [Hermann Weyl, placeOfBirth, Elmshorn]
-
A.
Delmenhorst
Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
-
B.
Hasselwerder
Hasselwerder is a small island located in Lake Tegel in Berlin, Germany.
-
C.
Langenhagen
Langenhagen is a town in Lower Saxony, Germany, located just north of Hanover and known for hosting part of Hanover Airport and various industrial and commercial facilities.
-
D.
Maienwerder
Maienwerder is a small island located in the Tegeler See lake in Berlin, Germany, known for its natural setting and limited accessibility.
-
E.
Lüneburg
Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
- 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: Elmshorn Triple: [Hermann Weyl, placeOfBirth, Elmshorn]
Generated description
Elmshorn is a town in northern Germany’s Schleswig-Holstein state, known as an industrial and commuter hub northwest of Hamburg.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Elmshorn Target entity description: Elmshorn is a town in northern Germany’s Schleswig-Holstein state, known as an industrial and commuter hub northwest of Hamburg.
-
A.
Delmenhorst
Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
-
B.
Hasselwerder
Hasselwerder is a small island located in Lake Tegel in Berlin, Germany.
-
C.
Langenhagen
Langenhagen is a town in Lower Saxony, Germany, located just north of Hanover and known for hosting part of Hanover Airport and various industrial and commercial facilities.
-
D.
Maienwerder
Maienwerder is a small island located in the Tegeler See lake in Berlin, Germany, known for its natural setting and limited accessibility.
-
E.
Lüneburg
Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
- 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_69a493c383dc8190a03257f22d4b4183 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b4ac27e081908f132115464667b2 |
completed | March 1, 2026, 9:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acce53fd1c81909b3715231ad09b91 |
completed | March 8, 2026, 1:18 a.m. |
| NEDg | Description generation | batch_69accef1f294819093f463001ae8796b |
completed | March 8, 2026, 1:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69accf59a4e48190bfe11b97c4d33913 |
completed | March 8, 2026, 1:22 a.m. |
Created at: March 1, 2026, 7:41 p.m.