Triple
T7361516
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bavaria Film |
E169759
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Marienhof
Marienhof is a German television soap opera that gained popularity in the 1990s and 2000s for its portrayal of everyday life and relationships in a fictional Cologne neighborhood.
|
E658041
|
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: Marienhof | Statement: [Bavaria Film, notableWork, Marienhof]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marienhof Context triple: [Bavaria Film, notableWork, Marienhof]
-
A.
Hartmannshof
Hartmannshof is a locality in Bavaria, Germany, that functions as an outer terminus on the Nuremberg S-Bahn commuter rail network.
-
B.
Riedergarten
Riedergarten is a historic public garden and popular green oasis located in the Bavarian city of Rosenheim, Germany.
-
C.
Rothe House
Rothe House is a well-preserved 16th-century merchant’s townhouse complex and museum in Kilkenny, Ireland, noted for its historic architecture and cultural significance.
-
D.
Almenhof
Almenhof is a residential district of Mannheim in the German state of Baden-Württemberg.
-
E.
Schickenhof
Schickenhof is a small locality in Germany best known as the birthplace of Nobel Prize–winning physicist Johannes Stark.
- 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: Marienhof Triple: [Bavaria Film, notableWork, Marienhof]
Generated description
Marienhof is a German television soap opera that gained popularity in the 1990s and 2000s for its portrayal of everyday life and relationships in a fictional Cologne neighborhood.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Marienhof Target entity description: Marienhof is a German television soap opera that gained popularity in the 1990s and 2000s for its portrayal of everyday life and relationships in a fictional Cologne neighborhood.
-
A.
Hartmannshof
Hartmannshof is a locality in Bavaria, Germany, that functions as an outer terminus on the Nuremberg S-Bahn commuter rail network.
-
B.
Riedergarten
Riedergarten is a historic public garden and popular green oasis located in the Bavarian city of Rosenheim, Germany.
-
C.
Rothe House
Rothe House is a well-preserved 16th-century merchant’s townhouse complex and museum in Kilkenny, Ireland, noted for its historic architecture and cultural significance.
-
D.
Almenhof
Almenhof is a residential district of Mannheim in the German state of Baden-Württemberg.
-
E.
Schickenhof
Schickenhof is a small locality in Germany best known as the birthplace of Nobel Prize–winning physicist Johannes Stark.
- 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_69c68a59f2288190877ca15c19b1e822 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f15e0280819086627cef15fe18bd |
completed | March 27, 2026, 9:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7fab3a9d88190b9193dd0729eba5a |
completed | March 28, 2026, 3:58 p.m. |
| NEDg | Description generation | batch_69c7fbe2a86881909be54dac809aa9af |
completed | March 28, 2026, 4:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c7fc84fff48190b4b43da21a9ede51 |
completed | March 28, 2026, 4:06 p.m. |
Created at: March 27, 2026, 3:06 p.m.