Triple
T22929897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alexa shopping mall |
E569406
|
entity |
| Predicate | district |
P2709
|
FINISHED |
| Object | Mitte |
—
|
NE NERFINISHED |
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: Mitte | Statement: [Alexa shopping mall, district, Mitte]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mitte Context triple: [Alexa shopping mall, district, Mitte]
-
A.
Mitte
chosen
Mitte is the central district of Berlin, Germany, known as the historic core of the city and home to many major landmarks and government institutions.
-
B.
Mitte
Mitte is a central urban district of the German city of Koblenz, encompassing key administrative, commercial, and historic areas.
-
C.
Mitte
Mitte is the central urban district of Saarbrücken, Germany, encompassing much of the city’s administrative, commercial, and cultural core.
-
D.
Mitte
Mitte is a central district of the German town of Schwerte, typically encompassing its historic core and main administrative and commercial areas.
-
E.
Mitte
Mitte is the central urban district of Ludwigshafen am Rhein, Germany, encompassing the city’s core commercial and administrative areas.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e2458f7d008190901dccbaebeaba24 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f180dc33e8819099e5ad87207de57f |
completed | April 29, 2026, 3:54 a.m. |
Created at: April 17, 2026, 3:44 p.m.