Triple
T20459238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erkner |
E501878
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Tornesch |
—
|
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: Tornesch | Statement: [Erkner, hasTwinTown, Tornesch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tornesch Context triple: [Erkner, hasTwinTown, Tornesch]
-
A.
Tornesch
chosen
Tornesch is a small town in the district of Pinneberg in Schleswig-Holstein, northern Germany, known for its residential character and proximity to Hamburg.
-
B.
Wülscheid
Wülscheid is a small locality within the Aegidienberg district of Bad Honnef in North Rhine-Westphalia, Germany.
-
C.
Radeberg
Radeberg is a small town in the German state of Saxony, known for its Radeberger Pilsner brewery and historic town center near Dresden.
-
D.
Oderberg
Oderberg is a small historic town in northeastern Germany near the Oder River, known for its scenic natural surroundings and proximity to the Polish border.
-
E.
Eschwege
Eschwege is a small historic town in the German state of Hesse, known for its medieval architecture and location near the Werra River.
- 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_69e0b4ad4940819098cf2ff6413574e5 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e696a4652c8190acf79fa2e285e436 |
completed | April 20, 2026, 9:12 p.m. |
Created at: April 16, 2026, 11:33 a.m.