Triple
T21383530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Białogard |
E527426
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Gadebusch |
—
|
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: Gadebusch | Statement: [Białogard, hasTwinTown, Gadebusch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gadebusch Context triple: [Białogard, hasTwinTown, Gadebusch]
-
A.
Gadebusch
chosen
Gadebusch is a small historic town in northern Germany known for its medieval architecture and rural surroundings.
-
B.
Odershausen
Odershausen is a village and district of the spa town Bad Wildungen in the state of Hesse, Germany.
-
C.
Bockau
Bockau is a small town in the Ore Mountains of Saxony, Germany, historically shaped by mining and traditional industries.
-
D.
Treuenbrietzen
Treuenbrietzen is a historic town in the German state of Brandenburg, known for its medieval architecture and role in Reformation-era history.
-
E.
Teterow
Teterow is a small historic town in northeastern Germany known for its medieval architecture and location in the Mecklenburg Lake District.
- 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_69e0b51f363c8190944000ab5523b02b |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b0f05278819096c511035ffc9777 |
completed | April 22, 2026, 11:28 a.m. |
Created at: April 16, 2026, 5:12 p.m.