Triple
T6842932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wittenau |
E157817
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Märkisches Viertel |
E392708
|
NE FINISHED |
How this triple was built (2 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: Märkisches Viertel | Statement: [Wittenau, borders, Märkisches Viertel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Märkisches Viertel Context triple: [Wittenau, borders, Märkisches Viertel]
-
A.
Märkisches Viertel
chosen
Märkisches Viertel is a large post-war housing estate and residential district in the Reinickendorf borough of Berlin, known for its high-rise apartment blocks and dense urban layout.
-
B.
Dorotheenstadt
Dorotheenstadt is a historic district in central Berlin, Germany, known for its cultural significance and notable institutions.
-
C.
Kreuzberg
Kreuzberg is a vibrant, historically working-class district in central Berlin known for its multicultural community, alternative culture, and lively arts and nightlife scenes.
-
D.
Kreuzberg
Kreuzberg is a prominent mountain in the Rhön range of central Germany, known for its monastery, pilgrimage site, and scenic hiking opportunities.
-
E.
Bornheim Mitte
Bornheim Mitte is a central public transit station in Frankfurt’s Bornheim district, serving as a key stop on the city’s U-Bahn network.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c6882ed4c081909dc465a7cf8838be |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d6b7179481909e3482fef47b2719 |
completed | March 27, 2026, 7:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c748b5a7c08190983bd355a1bc76d7 |
completed | March 28, 2026, 3:19 a.m. |
Created at: March 27, 2026, 2:19 p.m.