Triple
T3559686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | House of Brunswick-Bevern |
E75305
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object |
Bevern
Bevern is a municipality in Lower Saxony, Germany, historically associated with the ducal House of Brunswick-Bevern.
|
E370549
|
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: Bevern | Statement: [House of Brunswick-Bevern, namedAfter, Bevern]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bevern Context triple: [House of Brunswick-Bevern, namedAfter, Bevern]
-
A.
Bewdley
Bewdley is a historic riverside town in Worcestershire, England, known for its Georgian architecture and picturesque setting on the River Severn.
-
B.
Bainsford
Bainsford is a district in Falkirk, Scotland, historically associated with local industry and early Scottish football.
-
C.
Bardwell
Bardwell is a small rural village and civil parish in the county of Suffolk in eastern England.
-
D.
Boothferry
Boothferry was a former local government district in northern England that was later reorganized, with part of its area contributing to the formation of North Lincolnshire.
-
E.
Bordon
Bordon is a town in East Hampshire, England, historically known for its large army camp and military training facilities.
- 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: Bevern Triple: [House of Brunswick-Bevern, namedAfter, Bevern]
Generated description
Bevern is a municipality in Lower Saxony, Germany, historically associated with the ducal House of Brunswick-Bevern.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bevern Target entity description: Bevern is a municipality in Lower Saxony, Germany, historically associated with the ducal House of Brunswick-Bevern.
-
A.
Bewdley
Bewdley is a historic riverside town in Worcestershire, England, known for its Georgian architecture and picturesque setting on the River Severn.
-
B.
Bainsford
Bainsford is a district in Falkirk, Scotland, historically associated with local industry and early Scottish football.
-
C.
Bardwell
Bardwell is a small rural village and civil parish in the county of Suffolk in eastern England.
-
D.
Boothferry
Boothferry was a former local government district in northern England that was later reorganized, with part of its area contributing to the formation of North Lincolnshire.
-
E.
Bordon
Bordon is a town in East Hampshire, England, historically known for its large army camp and military training facilities.
- 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_69ad85d45090819086f34fb85d850a1e |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc089270c81908bc200c84fe1592e |
completed | March 8, 2026, 6:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3bb9de4bc8190ba5d111465e66cf8 |
completed | March 13, 2026, 7:24 a.m. |
| NEDg | Description generation | batch_69b3bf85f4f881908bfd2dcfaa3537af |
completed | March 13, 2026, 7:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b3fb35c3a08190b50c87923037ee07 |
completed | March 13, 2026, 11:55 a.m. |
Created at: March 8, 2026, 3:20 p.m.