Triple
T427695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Friedrich Engels |
E9644
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object |
Barmen
Barmen is a historic industrial district in the German city of Wuppertal, known as a former textile and manufacturing center in the Ruhr region.
|
E54311
|
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: Barmen | Statement: [Friedrich Engels, placeOfBirth, Barmen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Barmen Context triple: [Friedrich Engels, placeOfBirth, Barmen]
-
A.
Starnberg
Starnberg is a lakeside town in Bavaria, Germany, known for its affluent residential character and scenic location on Lake Starnberg southwest of Munich.
-
B.
Furth
A Furth is a mountain in the British Isles outside Scotland that meets the height and prominence criteria to be classified similarly to a Scottish Munro.
-
C.
Ahaus
Ahaus is a town in the district of Borken in North Rhine-Westphalia, western Germany, known for its historic castle and role as a regional administrative and cultural center.
-
D.
Boblingen
Böblingen is a town in the German state of Baden-Württemberg, known for its automotive industry presence and proximity to Stuttgart.
-
E.
Hamburg
Hamburg is Germany’s second-largest city and a major northern European port and cultural center on the River Elbe.
- 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: Barmen Triple: [Friedrich Engels, placeOfBirth, Barmen]
Generated description
Barmen is a historic industrial district in the German city of Wuppertal, known as a former textile and manufacturing center in the Ruhr region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Barmen Target entity description: Barmen is a historic industrial district in the German city of Wuppertal, known as a former textile and manufacturing center in the Ruhr region.
-
A.
Starnberg
Starnberg is a lakeside town in Bavaria, Germany, known for its affluent residential character and scenic location on Lake Starnberg southwest of Munich.
-
B.
Furth
A Furth is a mountain in the British Isles outside Scotland that meets the height and prominence criteria to be classified similarly to a Scottish Munro.
-
C.
Ahaus
Ahaus is a town in the district of Borken in North Rhine-Westphalia, western Germany, known for its historic castle and role as a regional administrative and cultural center.
-
D.
Boblingen
Böblingen is a town in the German state of Baden-Württemberg, known for its automotive industry presence and proximity to Stuttgart.
-
E.
Hamburg
Hamburg is Germany’s second-largest city and a major northern European port and cultural center on the River Elbe.
- 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_69a2e801e1d48190b505d1dd336b52ac |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2eed7f3508190995dcd39586ed614 |
completed | Feb. 28, 2026, 1:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a42f665c2881908850bce36cdf74b8 |
completed | March 1, 2026, 12:21 p.m. |
| NEDg | Description generation | batch_69a43038d2348190a348e6661d27dde4 |
completed | March 1, 2026, 12:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a430f6c6f88190b5aecfe3c4c8957d |
completed | March 1, 2026, 12:28 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.