Triple
T20345119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moers |
E495845
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Seelow |
—
|
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: Seelow | Statement: [Moers, hasTwinTown, Seelow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seelow Context triple: [Moers, hasTwinTown, Seelow]
-
A.
Seelow
chosen
Seelow is a small town in eastern Brandenburg, Germany, best known today as the administrative center of the Märkisch-Oderland district and for its proximity to the historic Seelow Heights battlefield of World War II.
-
B.
Schkopau
Schkopau is a municipality in the Saalekreis district of Saxony-Anhalt, Germany, known for its large chemical industry complex.
-
C.
Teesdorf
Teesdorf is a municipality in Lower Austria known for its motorsport testing facilities and rural setting south of Vienna.
-
D.
Degendorf
Degendorf is a locality within the Bavarian town and district of Lichtenfels in Germany.
-
E.
Beelitz
Beelitz is a small German town in the state of Brandenburg, best known for its historic asparagus cultivation and the nearby Beelitz-Heilstätten sanatorium complex.
- 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_69e0b4a3320881909495ae8bc30bc2dc |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67838744481909069b76b25dd4bb9 |
completed | April 20, 2026, 7:02 p.m. |
Created at: April 16, 2026, 11:24 a.m.