Triple
T599675
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saxony |
E11465
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Meissen
Meissen is a historic town in eastern Germany renowned for its medieval architecture and as the birthplace of European hard-paste porcelain.
|
E74716
|
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: Meissen | Statement: [Saxony, contains, Meissen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meissen Context triple: [Saxony, contains, Meissen]
-
A.
Dresden
Dresden is a historic cultural and economic center in eastern Germany, renowned for its baroque architecture, art collections, and reconstruction after World War II.
-
B.
Chemnitz
Chemnitz is a city in eastern Germany known for its industrial heritage and post-reunification urban redevelopment.
-
C.
Saxony
Saxony is a historic region and former kingdom in eastern Germany, known for its cultural centers like Dresden and Leipzig and its significant role in Central European history.
-
D.
Leipzig
Leipzig is a major city in eastern Germany known for its rich cultural heritage, vibrant music and arts scene, and important role in trade and commerce.
-
E.
Jena
Jena is a historic university city in the German state of Thuringia, known for its role in optics, philosophy, and science.
- 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: Meissen Triple: [Saxony, contains, Meissen]
Generated description
Meissen is a historic town in eastern Germany renowned for its medieval architecture and as the birthplace of European hard-paste porcelain.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Meissen Target entity description: Meissen is a historic town in eastern Germany renowned for its medieval architecture and as the birthplace of European hard-paste porcelain.
-
A.
Dresden
Dresden is a historic cultural and economic center in eastern Germany, renowned for its baroque architecture, art collections, and reconstruction after World War II.
-
B.
Chemnitz
Chemnitz is a city in eastern Germany known for its industrial heritage and post-reunification urban redevelopment.
-
C.
Saxony
Saxony is a historic region and former kingdom in eastern Germany, known for its cultural centers like Dresden and Leipzig and its significant role in Central European history.
-
D.
Leipzig
Leipzig is a major city in eastern Germany known for its rich cultural heritage, vibrant music and arts scene, and important role in trade and commerce.
-
E.
Jena
Jena is a historic university city in the German state of Thuringia, known for its role in optics, philosophy, and science.
- 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_69a4932779b881908688590d59c71900 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49d78c0f08190b83ad89062ccb0b9 |
completed | March 1, 2026, 8:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a51f37f8748190bff705fd2bbc489c |
completed | March 2, 2026, 5:25 a.m. |
| NEDg | Description generation | batch_69a51fd2fe2881909a2cddf344149eeb |
completed | March 2, 2026, 5:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a52036174c81909a1e2aabad8fdad1 |
completed | March 2, 2026, 5:29 a.m. |
Created at: March 1, 2026, 7:35 p.m.