Triple
T13112067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brandenburg-Prussia |
E310995
|
entity |
| Predicate | hasTerritory |
P285
|
FINISHED |
| Object |
Ravensberg
Ravensberg was a historical county in northwestern Germany that became part of the expanding territorial holdings of Brandenburg-Prussia.
|
E1021524
|
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: Ravensberg | Statement: [Brandenburg-Prussia, hasTerritory, Ravensberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ravensberg Context triple: [Brandenburg-Prussia, hasTerritory, Ravensberg]
-
A.
Batenburg
Batenburg is a small historic town in the Dutch province of Gelderland, known for its medieval castle ruins and picturesque setting along the river Maas.
-
B.
Greifelt
Greifelt is a German surname most notably associated with Ulrich Greifelt, a high-ranking official in Nazi Germany.
-
C.
Havelterberg
Havelterberg is a modest hill and natural area in the Dutch province of Drenthe, known for its scenic landscapes and prehistoric burial mounds.
-
D.
Wassenberg
Wassenberg is a historic town in western Germany near the Dutch border, known for its medieval origins and association with the noble House of Wassenberg.
-
E.
Löwenberg
Löwenberg is a town in Germany known for its cultural and municipal partnership as a twin town of Weilburg.
- 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: Ravensberg Triple: [Brandenburg-Prussia, hasTerritory, Ravensberg]
Generated description
Ravensberg was a historical county in northwestern Germany that became part of the expanding territorial holdings of Brandenburg-Prussia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ravensberg Target entity description: Ravensberg was a historical county in northwestern Germany that became part of the expanding territorial holdings of Brandenburg-Prussia.
-
A.
Batenburg
Batenburg is a small historic town in the Dutch province of Gelderland, known for its medieval castle ruins and picturesque setting along the river Maas.
-
B.
Greifelt
Greifelt is a German surname most notably associated with Ulrich Greifelt, a high-ranking official in Nazi Germany.
-
C.
Havelterberg
Havelterberg is a modest hill and natural area in the Dutch province of Drenthe, known for its scenic landscapes and prehistoric burial mounds.
-
D.
Wassenberg
Wassenberg is a historic town in western Germany near the Dutch border, known for its medieval origins and association with the noble House of Wassenberg.
-
E.
Löwenberg
Löwenberg is a town in Germany known for its cultural and municipal partnership as a twin town of Weilburg.
- 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_69d806a872d08190a329806f8ff30df4 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d9817f8ee8819084078b4bec5e4f18 |
completed | April 10, 2026, 11:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6e27f5c4481909bc323c9d0c83dc9 |
completed | May 3, 2026, 5:51 a.m. |
| NEDg | Description generation | batch_69f6e32bf5508190b4dc58971f8f64d0 |
completed | May 3, 2026, 5:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6e407dd988190b928b8931985a815 |
completed | May 3, 2026, 5:58 a.m. |
Created at: April 9, 2026, 9:05 p.m.