Triple
T3381544
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naturkundemuseum |
E71195
|
entity |
| Predicate | locatedUnder |
P10157
|
FINISHED |
| Object | Chausseestraße |
E327889
|
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: Chausseestraße | Statement: [Naturkundemuseum, locatedUnder, Chausseestraße]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chausseestraße Context triple: [Naturkundemuseum, locatedUnder, Chausseestraße]
-
A.
Chausseestraße
chosen
Chausseestraße is a major historic street in Berlin, Germany, known for its cultural landmarks and central location.
-
B.
Friedrichstraße
Friedrichstraße is a major central Berlin transport hub and historic thoroughfare known for its shopping, cultural venues, and role as a former border crossing during the Cold War.
-
C.
Paradestraße
Paradestraße is a Berlin U-Bahn station on the north–south route in the Tempelhof-Schöneberg district, known for serving the U6 line.
-
D.
Kaufingerstraße
Kaufingerstraße is one of Munich’s main and oldest pedestrian shopping streets, lined with stores and historic buildings in the city center.
-
E.
Scharnweberstraße
Scharnweberstraße is a station on Berlin’s U6 U-Bahn line serving the Reinickendorf district in the north of the city.
- 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_69ad85a8fd9c819095ecedf838d2bf1b |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb5e9af608190bfb228ef99a87bb7 |
completed | March 8, 2026, 5:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b360aded3c8190ab4ca37b4aead1df |
completed | March 13, 2026, 12:56 a.m. |
Created at: March 8, 2026, 3:14 p.m.