Triple
T7813810
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unter den Linden |
E180751
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Lustgarten area |
E106565
|
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: Lustgarten area | Statement: [Unter den Linden, connectsTo, Lustgarten area]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lustgarten area Context triple: [Unter den Linden, connectsTo, Lustgarten area]
-
A.
Lustgarten
chosen
Lustgarten is a historic public park and square on Berlin’s Museum Island, long used as a parade ground and gathering place.
-
B.
Drusberg area
The Drusberg area is a mountainous region in the Swiss Alps known for its rugged terrain and alpine landscapes.
-
C.
Vollererhof area
The Vollererhof area is a locality within the municipality of Puch bei Hallein in the Austrian state of Salzburg, known for its scenic setting in the northern Alps.
-
D.
Michlifen area
The Michlifen area is a mountainous resort region in Morocco’s Middle Atlas, known for its ski slopes, cedar forests, and cool alpine climate near the town of Azrou.
-
E.
Botlek area
The Botlek area is an industrial and port district within the Port of Rotterdam, known for its large petrochemical complexes and heavy maritime logistics activities.
- 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_69ca827f6f148190beca4e245b993506 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69caf78f3d6481909841d64117f657e1 |
completed | March 30, 2026, 10:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb1472ee908190b073819f3dfad8ee |
completed | March 31, 2026, 12:25 a.m. |
Created at: March 30, 2026, 4:38 p.m.