Triple
T15233869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Möhringen |
E364072
|
entity |
| Predicate | hasSubdistrict |
P747
|
FINISHED |
| Object |
Fasanenhof
Fasanenhof is a residential subdistrict of Stuttgart’s Möhringen district in the German state of Baden-Württemberg.
|
E1144292
|
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: Fasanenhof | Statement: [Möhringen, hasSubdistrict, Fasanenhof]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fasanenhof Context triple: [Möhringen, hasSubdistrict, Fasanenhof]
-
A.
Scheibenhof
Scheibenhof is a locality or district that forms part of the city of Krems an der Donau in Lower Austria.
-
B.
Hartmannshof
Hartmannshof is a locality in Bavaria, Germany, that functions as an outer terminus on the Nuremberg S-Bahn commuter rail network.
-
C.
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.
-
D.
Falkeplatz
Falkeplatz is a location in Chemnitz, Germany, known for hosting cultural institutions such as the Museum Gunzenhauser.
-
E.
Grabenhof
Grabenhof is a notable building complex located on Vienna’s historic Graben street, known for its characteristic 19th-century architecture and prominent city-center location.
- 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: Fasanenhof Triple: [Möhringen, hasSubdistrict, Fasanenhof]
Generated description
Fasanenhof is a residential subdistrict of Stuttgart’s Möhringen district in the German state of Baden-Württemberg.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fasanenhof Target entity description: Fasanenhof is a residential subdistrict of Stuttgart’s Möhringen district in the German state of Baden-Württemberg.
-
A.
Scheibenhof
Scheibenhof is a locality or district that forms part of the city of Krems an der Donau in Lower Austria.
-
B.
Hartmannshof
Hartmannshof is a locality in Bavaria, Germany, that functions as an outer terminus on the Nuremberg S-Bahn commuter rail network.
-
C.
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.
-
D.
Falkeplatz
Falkeplatz is a location in Chemnitz, Germany, known for hosting cultural institutions such as the Museum Gunzenhauser.
-
E.
Grabenhof
Grabenhof is a notable building complex located on Vienna’s historic Graben street, known for its characteristic 19th-century architecture and prominent city-center location.
- 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_69d85a0ce24c81909c4d3b6475548c95 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e007d7237081908dc17900ee66b64f |
completed | April 15, 2026, 9:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fedd3dd5a081909a1a7fceda648c29 |
completed | May 9, 2026, 7:07 a.m. |
| NEDg | Description generation | batch_69fede8eed2c8190adb45306a1ec0faa |
completed | May 9, 2026, 7:13 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fedef546708190bdeedd2c61fdbc86 |
completed | May 9, 2026, 7:15 a.m. |
Created at: April 10, 2026, 3:12 a.m.