Triple
T1624712
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | District of Leipzig |
E35114
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Markkleeberg
Markkleeberg is a town in the German state of Saxony known for its proximity to Leipzig and its recreational lakes and green spaces.
|
E226311
|
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: Markkleeberg | Statement: [District of Leipzig, contains, Markkleeberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Markkleeberg Context triple: [District of Leipzig, contains, Markkleeberg]
-
A.
Degendorf
Degendorf is a locality within the Bavarian town and district of Lichtenfels in Germany.
-
B.
Lünen
Lünen is a town in North Rhine-Westphalia, Germany, known as an industrial and commuter city in the Ruhr area.
-
C.
Tureberg
Tureberg is a central district in Sollentuna Municipality, Sweden, known for housing the municipal center and key public services.
-
D.
Bergedorf
Bergedorf is a historic quarter and former independent town in the southeast of Hamburg, Germany, known for its medieval castle and role as a regional administrative and trading center.
-
E.
Ronsdorf
Ronsdorf is a district of the German city of Wuppertal in North Rhine-Westphalia, historically known as an independent town in the Bergisches Land region.
- 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: Markkleeberg Triple: [District of Leipzig, contains, Markkleeberg]
Generated description
Markkleeberg is a town in the German state of Saxony known for its proximity to Leipzig and its recreational lakes and green spaces.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Markkleeberg Target entity description: Markkleeberg is a town in the German state of Saxony known for its proximity to Leipzig and its recreational lakes and green spaces.
-
A.
Degendorf
Degendorf is a locality within the Bavarian town and district of Lichtenfels in Germany.
-
B.
Lünen
Lünen is a town in North Rhine-Westphalia, Germany, known as an industrial and commuter city in the Ruhr area.
-
C.
Tureberg
Tureberg is a central district in Sollentuna Municipality, Sweden, known for housing the municipal center and key public services.
-
D.
Bergedorf
Bergedorf is a historic quarter and former independent town in the southeast of Hamburg, Germany, known for its medieval castle and role as a regional administrative and trading center.
-
E.
Ronsdorf
Ronsdorf is a district of the German city of Wuppertal in North Rhine-Westphalia, historically known as an independent town in the Bergisches Land region.
- 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_69a886023194819080a3fccd6e325d0e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a909d0586c81909e399b636e130ff5 |
completed | March 5, 2026, 4:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae0aaae76c81909707184b3a3d87d5 |
completed | March 8, 2026, 11:47 p.m. |
| NEDg | Description generation | batch_69ae0b49abfc81908876ea54c7b7dcc2 |
completed | March 8, 2026, 11:50 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae0d1bb5c881908c27bdd359e78773 |
completed | March 8, 2026, 11:58 p.m. |
Created at: March 4, 2026, 7:28 p.m.