Triple
T11011718
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cloppenburg (district) |
E260262
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Essen (Oldenburg)
Essen (Oldenburg) is a small municipality in Lower Saxony, Germany, known for its rural character and location within the Cloppenburg district.
|
E899562
|
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: Essen (Oldenburg) | Statement: [Cloppenburg (district), hasMunicipality, Essen (Oldenburg)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Essen (Oldenburg) Context triple: [Cloppenburg (district), hasMunicipality, Essen (Oldenburg)]
-
A.
Osnabrück
Osnabrück is a historic city in Lower Saxony, Germany, known for its medieval architecture and role in the Peace of Westphalia.
-
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.
Gütersloh
Gütersloh is a city in the German state of North Rhine-Westphalia known for being the headquarters of major companies like Bertelsmann and Miele.
-
D.
Northeim
Northeim is a town in Lower Saxony, Germany, known for its medieval old town and location in the Leine River valley.
-
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: Essen (Oldenburg) Triple: [Cloppenburg (district), hasMunicipality, Essen (Oldenburg)]
Generated description
Essen (Oldenburg) is a small municipality in Lower Saxony, Germany, known for its rural character and location within the Cloppenburg district.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Essen (Oldenburg) Target entity description: Essen (Oldenburg) is a small municipality in Lower Saxony, Germany, known for its rural character and location within the Cloppenburg district.
-
A.
Osnabrück
Osnabrück is a historic city in Lower Saxony, Germany, known for its medieval architecture and role in the Peace of Westphalia.
-
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.
Gütersloh
Gütersloh is a city in the German state of North Rhine-Westphalia known for being the headquarters of major companies like Bertelsmann and Miele.
-
D.
Northeim
Northeim is a town in Lower Saxony, Germany, known for its medieval old town and location in the Leine River valley.
-
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_69d6aa9687448190b28d353b1b6a610e |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7978a57a881909b4ceae0ebe21b78 |
completed | April 9, 2026, 12:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e374ac78348190a8c0a5a7a736b24b |
completed | April 18, 2026, 12:10 p.m. |
| NEDg | Description generation | batch_69e378df767c819099d0bfdf35eaf5f3 |
completed | April 18, 2026, 12:28 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e37bf526108190b5fc22569fe6be54 |
completed | April 18, 2026, 12:41 p.m. |
Created at: April 8, 2026, 9:25 p.m.