Triple
T6316756
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kolbermoor |
E141634
|
entity |
| Predicate | hasRailConnectionTo |
P848
|
FINISHED |
| Object | Munich (via Rosenheim) |
E21335
|
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: Munich (via Rosenheim) | Statement: [Kolbermoor, hasRailConnectionTo, Munich (via Rosenheim)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Munich (via Rosenheim) Context triple: [Kolbermoor, hasRailConnectionTo, Munich (via Rosenheim)]
-
A.
Fürstenfeldbruck
Fürstenfeldbruck is a town in Upper Bavaria, Germany, known for its historic monastery, proximity to Munich, and nearby air base.
-
B.
Rosenheim
Rosenheim is a town in Upper Bavaria, Germany, known as a regional economic and transportation hub near the Alps.
-
C.
Munich
chosen
Munich is the capital and largest city of the German state of Bavaria, renowned for its rich cultural scene, historic architecture, and the annual Oktoberfest beer festival.
-
D.
Berlin–Munich
Berlin–Munich is a major high-speed rail corridor in Germany connecting the capital with Bavaria’s largest city.
-
E.
Bad Kissingen
Bad Kissingen is a historic spa town in northern Bavaria, Germany, renowned for its mineral springs and 19th-century wellness resorts.
- 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_69c008d13b8c8190be47d896eb735605 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c064c25530819080b29e0029175c00 |
completed | March 22, 2026, 9:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c5e478987c819085df63dab784af2a |
completed | March 27, 2026, 1:59 a.m. |
Created at: March 22, 2026, 4:29 p.m.