Triple
T4702446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bytom |
E104306
|
entity |
| Predicate | germanName |
P6492
|
FINISHED |
| Object |
Beuthen
Beuthen is the historical German name for the city of Bytom in southern Poland’s Upper Silesia region.
|
E462827
|
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: Beuthen | Statement: [Bytom, germanName, Beuthen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beuthen Context triple: [Bytom, germanName, Beuthen]
-
A.
Schkopau
Schkopau is a municipality in the Saalekreis district of Saxony-Anhalt, Germany, known for its large chemical industry complex.
-
B.
Lippendorf
Lippendorf is a village in Saxony, Germany, historically notable as the birthplace of Katharina von Bora, the wife of Martin Luther.
-
C.
Gehrden
Gehrden is a small town in Lower Saxony, Germany, located near Hanover and known for its surrounding rural villages and scenic landscapes.
-
D.
Lichterfelde
Lichterfelde is a residential district in southwestern Berlin known for its historic villas, leafy streets, and affluent character.
-
E.
Bernau bei Berlin
Bernau bei Berlin is a historic town in the German state of Brandenburg, located just northeast of Berlin and known for its well-preserved medieval city walls.
- 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: Beuthen Triple: [Bytom, germanName, Beuthen]
Generated description
Beuthen is the historical German name for the city of Bytom in southern Poland’s Upper Silesia region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Beuthen Target entity description: Beuthen is the historical German name for the city of Bytom in southern Poland’s Upper Silesia region.
-
A.
Schkopau
Schkopau is a municipality in the Saalekreis district of Saxony-Anhalt, Germany, known for its large chemical industry complex.
-
B.
Lippendorf
Lippendorf is a village in Saxony, Germany, historically notable as the birthplace of Katharina von Bora, the wife of Martin Luther.
-
C.
Gehrden
Gehrden is a small town in Lower Saxony, Germany, located near Hanover and known for its surrounding rural villages and scenic landscapes.
-
D.
Lichterfelde
Lichterfelde is a residential district in southwestern Berlin known for its historic villas, leafy streets, and affluent character.
-
E.
Bernau bei Berlin
Bernau bei Berlin is a historic town in the German state of Brandenburg, located just northeast of Berlin and known for its well-preserved medieval city walls.
- 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_69bd43e9b88481908582103dcadff3d9 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd63cec7988190b5f1d04d4f95314a |
completed | March 20, 2026, 3:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be03cd8cd4819081a7988b5945067f |
completed | March 21, 2026, 2:34 a.m. |
| NEDg | Description generation | batch_69be04c5549c819087204ac7e2e0e8ea |
completed | March 21, 2026, 2:39 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be05970dcc8190a86771d09f27d9f2 |
completed | March 21, 2026, 2:42 a.m. |
Created at: March 20, 2026, 1:17 p.m.