Triple
T1862375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ems |
E34843
|
entity |
| Predicate | majorCityOnRiver |
P316
|
FINISHED |
| Object |
Papenburg
Papenburg is a German town in Lower Saxony best known for its historic canals and its large Meyer Werft shipyard, one of the world’s leading builders of cruise ships.
|
E231594
|
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: Papenburg | Statement: [Ems, majorCityOnRiver, Papenburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Papenburg Context triple: [Ems, majorCityOnRiver, Papenburg]
-
A.
Nienburg
Nienburg is a historic town in Lower Saxony, Germany, known for its medieval architecture and scenic location along the Weser River.
-
B.
Hasselwerder
Hasselwerder is a small island located in Lake Tegel in Berlin, Germany.
-
C.
Walsrode
Walsrode is a small town in Lower Saxony, Germany, known for its location in the Lüneburg Heath region and its large bird park, the Weltvogelpark Walsrode.
-
D.
Lüneburg
Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
-
E.
Delmenhorst
Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
- 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: Papenburg Triple: [Ems, majorCityOnRiver, Papenburg]
Generated description
Papenburg is a German town in Lower Saxony best known for its historic canals and its large Meyer Werft shipyard, one of the world’s leading builders of cruise ships.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Papenburg Target entity description: Papenburg is a German town in Lower Saxony best known for its historic canals and its large Meyer Werft shipyard, one of the world’s leading builders of cruise ships.
-
A.
Nienburg
Nienburg is a historic town in Lower Saxony, Germany, known for its medieval architecture and scenic location along the Weser River.
-
B.
Hasselwerder
Hasselwerder is a small island located in Lake Tegel in Berlin, Germany.
-
C.
Walsrode
Walsrode is a small town in Lower Saxony, Germany, known for its location in the Lüneburg Heath region and its large bird park, the Weltvogelpark Walsrode.
-
D.
Lüneburg
Lüneburg is a historic Hanseatic town in northern Germany renowned for its medieval architecture and former wealth from salt mining.
-
E.
Delmenhorst
Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
- 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_69a88600b2f88190bc09303e68ab517e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abb09e714881909cef0f7e77b5b3b9 |
completed | March 7, 2026, 4:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae26f6e1288190a69d4197ce5bd5b1 |
completed | March 9, 2026, 1:48 a.m. |
| NEDg | Description generation | batch_69ae2901eb588190863e15deb8614754 |
completed | March 9, 2026, 1:57 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae297ebf6c819086e10ee455bea988 |
completed | March 9, 2026, 1:59 a.m. |
Created at: March 4, 2026, 7:34 p.m.