Triple
T21287769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diepholz district |
E524705
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Emtinghausen
Emtinghausen is a small municipality in Lower Saxony, Germany, known for its rural character within the Diepholz district.
|
E1476238
|
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: Emtinghausen | Statement: [Diepholz district, contains, Emtinghausen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Emtinghausen Context triple: [Diepholz district, contains, Emtinghausen]
-
A.
Fahrenkopf
Fahrenkopf is a surname most prominently associated with Frank J. Fahrenkopf Jr., an American lawyer, lobbyist, and former chairman of the Republican National Committee.
-
B.
Hartenstein
Hartenstein is a small town in the Free State of Saxony in eastern Germany, known for its historic architecture and scenic location near the Ore Mountains.
-
C.
Dannhauser
Dannhauser is a small town and local municipality in KwaZulu-Natal, South Africa, known historically for coal mining and agriculture.
-
D.
Spangenberg
Spangenberg is a small town in Germany, historically situated within the region of Westphalia.
-
E.
Lippinghausen
Lippinghausen is a locality within the municipality of Hiddenhausen in the German state of North Rhine-Westphalia.
- 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: Emtinghausen Triple: [Diepholz district, contains, Emtinghausen]
Generated description
Emtinghausen is a small municipality in Lower Saxony, Germany, known for its rural character within the Diepholz district.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Emtinghausen Target entity description: Emtinghausen is a small municipality in Lower Saxony, Germany, known for its rural character within the Diepholz district.
-
A.
Fahrenkopf
Fahrenkopf is a surname most prominently associated with Frank J. Fahrenkopf Jr., an American lawyer, lobbyist, and former chairman of the Republican National Committee.
-
B.
Hartenstein
Hartenstein is a small town in the Free State of Saxony in eastern Germany, known for its historic architecture and scenic location near the Ore Mountains.
-
C.
Dannhauser
Dannhauser is a small town and local municipality in KwaZulu-Natal, South Africa, known historically for coal mining and agriculture.
-
D.
Spangenberg
Spangenberg is a small town in Germany, historically situated within the region of Westphalia.
-
E.
Lippinghausen
Lippinghausen is a locality within the municipality of Hiddenhausen in the German state of North Rhine-Westphalia.
- 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_69e0b5171f6c8190a5d57201ede73811 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e736d7c57c8190bc4180ea590a62d4 |
completed | April 21, 2026, 8:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a099809b3d88190b78ddeae641a7abf |
completed | May 17, 2026, 10:27 a.m. |
| NEDg | Description generation | batch_6a0998f27c6c81908a88a4a831445d57 |
completed | May 17, 2026, 10:31 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09996c8d7c81908cc9874f6e1a3fff |
completed | May 17, 2026, 10:33 a.m. |
Created at: April 16, 2026, 4:03 p.m.