Triple
T3686001
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spandau |
E78225
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object | Siemensstadt |
E361710
|
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: Siemensstadt | Statement: [Spandau, hasSubdivision, Siemensstadt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siemensstadt Context triple: [Spandau, hasSubdivision, Siemensstadt]
-
A.
Siemensstadt
chosen
Siemensstadt is a Berlin neighborhood historically shaped by the Siemens industrial works and noted for its early 20th-century modernist housing developments.
-
B.
Lippendorf
Lippendorf is a village in Saxony, Germany, historically notable as the birthplace of Katharina von Bora, the wife of Martin Luther.
-
C.
Duderstadt
Duderstadt is a historic small town in southern Lower Saxony, Germany, known for its well-preserved medieval timber-framed architecture and role as a regional center in the Eichsfeld area.
-
D.
Bockenheim
Bockenheim is a lively urban district of Frankfurt am Main known for its mix of residential areas, shops, and university facilities.
-
E.
Grevesmühlen
Grevesmühlen is a small town in the German state of Mecklenburg-Vorpommern, known as a local administrative and service center in the north of the country.
- 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_69ad85e285a081908f8cbfa9e2ed9b75 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc4c676748190b074abfb9ba43b49 |
completed | March 8, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4c3bd473c8190b814689f3c76cada |
completed | March 14, 2026, 2:11 a.m. |
Created at: March 8, 2026, 3:26 p.m.