Triple
T14932555
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Litoměřice |
E372303
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Mölln |
E1058582
|
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: Mölln | Statement: [Litoměřice, twinTown, Mölln]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mölln Context triple: [Litoměřice, twinTown, Mölln]
-
A.
Mölln
chosen
Mölln is a historic town in northern Germany, known for its medieval center and association with the folk figure Till Eulenspiegel.
-
B.
Güstrow
Güstrow is a historic town in northern Germany known for its Renaissance castle, brick Gothic cathedral, and association with sculptor Ernst Barlach.
-
C.
Himmerich
Himmerich is a hill in Germany’s Siebengebirge range, known for its forested slopes and hiking trails overlooking the Rhine valley.
-
D.
Havelberg
Havelberg is a small historic town in Saxony-Anhalt, Germany, known for its medieval cathedral and location at the confluence of the Havel and Elbe rivers.
-
E.
Eckernförde
Eckernförde is a coastal town in northern Germany known for its Baltic Sea beaches, historic harbor, and maritime tourism.
- 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_69d85cc9da0c81908d583ca3f63a3908 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded646a0808190ba5c0c91bde011c5 |
completed | April 15, 2026, 12:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe72c86b5c81909e601a3fc78276bd |
completed | May 8, 2026, 11:33 p.m. |
Created at: April 10, 2026, 2:37 a.m.