Triple
T21996268
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bezirk Cottbus |
E543213
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Bad Liebenwerda
Bad Liebenwerda is a small spa town in the state of Brandenburg in eastern Germany, known for its mineral springs and health tourism.
|
E1511578
|
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: Bad Liebenwerda | Statement: [Bezirk Cottbus, contains, Bad Liebenwerda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bad Liebenwerda Context triple: [Bezirk Cottbus, contains, Bad Liebenwerda]
-
A.
Bad Belzig
Bad Belzig is a historic spa town in the German state of Brandenburg known for its medieval castle and thermal baths.
-
B.
Schkopau
Schkopau is a municipality in the Saalekreis district of Saxony-Anhalt, Germany, known for its large chemical industry complex.
-
C.
Bad Tölz
Bad Tölz is a Bavarian spa town in southern Germany known for its historic old town, alpine scenery, and traditional German architecture.
-
D.
Lichterfelde
Lichterfelde is a residential district in southwestern Berlin known for its historic villas, leafy streets, and affluent character.
-
E.
Mahlsdorf
Mahlsdorf is a locality in the borough of Marzahn-Hellersdorf in eastern Berlin, Germany, known for its residential character and historic village center.
- 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: Bad Liebenwerda Triple: [Bezirk Cottbus, contains, Bad Liebenwerda]
Generated description
Bad Liebenwerda is a small spa town in the state of Brandenburg in eastern Germany, known for its mineral springs and health tourism.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bad Liebenwerda Target entity description: Bad Liebenwerda is a small spa town in the state of Brandenburg in eastern Germany, known for its mineral springs and health tourism.
-
A.
Bad Belzig
Bad Belzig is a historic spa town in the German state of Brandenburg known for its medieval castle and thermal baths.
-
B.
Schkopau
Schkopau is a municipality in the Saalekreis district of Saxony-Anhalt, Germany, known for its large chemical industry complex.
-
C.
Bad Tölz
Bad Tölz is a Bavarian spa town in southern Germany known for its historic old town, alpine scenery, and traditional German architecture.
-
D.
Lichterfelde
Lichterfelde is a residential district in southwestern Berlin known for its historic villas, leafy streets, and affluent character.
-
E.
Mahlsdorf
Mahlsdorf is a locality in the borough of Marzahn-Hellersdorf in eastern Berlin, Germany, known for its residential character and historic village center.
- 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_69e11e2c814c8190837d072789000486 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f12765fb0c81908f7b7acda065ee2f |
completed | April 28, 2026, 9:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a6d87e33481909d2d795b2070fef3 |
completed | May 18, 2026, 1:38 a.m. |
| NEDg | Description generation | batch_6a0a6e05677c819082f0613fbf6aeff0 |
completed | May 18, 2026, 1:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a6e93dfe88190bcddd1802017ddea |
completed | May 18, 2026, 1:42 a.m. |
Created at: April 16, 2026, 8:19 p.m.