Triple
T3317585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Innere Stadt |
E69717
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object |
Alsergrund
Alsergrund is the 9th district of Vienna, Austria, known for its historic architecture, cultural institutions, and proximity to the city center.
|
E348815
|
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: Alsergrund | Statement: [Innere Stadt, borderedBy, Alsergrund]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alsergrund Context triple: [Innere Stadt, borderedBy, Alsergrund]
-
A.
Riedergarten
Riedergarten is a historic public garden and popular green oasis located in the Bavarian city of Rosenheim, Germany.
-
B.
Adlershof
Adlershof is a district in Berlin, Germany, known as a major science, technology, and media hub featuring research institutes, universities, and high-tech companies.
-
C.
Gartenstadt
Gartenstadt is a residential district of the Upper Franconian town of Lichtenfels in Bavaria, Germany.
-
D.
Marienfelde
Marienfelde is a locality in the southern part of Berlin known for its residential areas and historical refugee reception center.
-
E.
Bayerisches Viertel
Bayerisches Viertel is a historic residential neighborhood in Berlin known for its early 20th-century architecture and its significant Jewish cultural and memorial heritage.
- 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: Alsergrund Triple: [Innere Stadt, borderedBy, Alsergrund]
Generated description
Alsergrund is the 9th district of Vienna, Austria, known for its historic architecture, cultural institutions, and proximity to the city center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Alsergrund Target entity description: Alsergrund is the 9th district of Vienna, Austria, known for its historic architecture, cultural institutions, and proximity to the city center.
-
A.
Riedergarten
Riedergarten is a historic public garden and popular green oasis located in the Bavarian city of Rosenheim, Germany.
-
B.
Adlershof
Adlershof is a district in Berlin, Germany, known as a major science, technology, and media hub featuring research institutes, universities, and high-tech companies.
-
C.
Gartenstadt
Gartenstadt is a residential district of the Upper Franconian town of Lichtenfels in Bavaria, Germany.
-
D.
Marienfelde
Marienfelde is a locality in the southern part of Berlin known for its residential areas and historical refugee reception center.
-
E.
Bayerisches Viertel
Bayerisches Viertel is a historic residential neighborhood in Berlin known for its early 20th-century architecture and its significant Jewish cultural and memorial heritage.
- 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_69ad85a0bb048190a5458d2738012d61 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb113cb6c8190989b06476f6015fd |
completed | March 8, 2026, 5:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b31a759a3c81908105eae7fd856133 |
completed | March 12, 2026, 7:56 p.m. |
| NEDg | Description generation | batch_69b31c34cc388190a5fab8e9b2a1aa92 |
completed | March 12, 2026, 8:04 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b31da025048190b7d1611df82a542c |
completed | March 12, 2026, 8:10 p.m. |
Created at: March 8, 2026, 3:11 p.m.