Triple
T8995955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coburg |
E214909
|
entity |
| Predicate | hasRailConnectionTo |
P848
|
FINISHED |
| Object | Sonneberg |
E228673
|
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: Sonneberg | Statement: [Coburg, hasRailConnectionTo, Sonneberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sonneberg Context triple: [Coburg, hasRailConnectionTo, Sonneberg]
-
A.
Sonneberg
chosen
Sonneberg is a town in the German state of Thuringia, historically known for its toy-making industry and museums.
-
B.
Breckerfeld
Breckerfeld is a small town in North Rhine-Westphalia, Germany, known for its rural character and location in the hilly, forested region of the Sauerland.
-
C.
Braunsberg
Braunsberg is a locality in former East Prussia (now in Poland) known for its proximity to the World War II Heiligenbeil pocket battlefield.
-
D.
Günsberg
Günsberg is a Swiss municipality located in the canton of Solothurn, known for its scenic setting near the Jura Mountains.
-
E.
Gevelsberg
Gevelsberg is a town in North Rhine-Westphalia, Germany, situated in the Ennepe-Ruhr district within the Ruhr metropolitan region.
- 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_69ca83a05c608190bdfdbdb25e994b39 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc68df33c48190a5017426e59c0bc4 |
completed | April 1, 2026, 12:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfdb9531748190bd710e0b386b2cbe |
completed | April 3, 2026, 3:24 p.m. |
Created at: March 30, 2026, 7:04 p.m.