Triple
T17147955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aulendorf–Kißlegg railway |
E416142
|
entity |
| Predicate | servesSettlement |
P2741
|
FINISHED |
| Object | Kißlegg |
E1251991
|
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: Kißlegg | Statement: [Aulendorf–Kißlegg railway, servesSettlement, Kißlegg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kißlegg Context triple: [Aulendorf–Kißlegg railway, servesSettlement, Kißlegg]
-
A.
Kißlegg
chosen
Kißlegg is a small town in the Ravensburg district of Baden-Württemberg, Germany, known for its historic castles and location in the scenic Allgäu region.
-
B.
Tihany
Tihany is a historic village on the northern shore of Lake Balaton in Hungary, renowned for its Benedictine abbey, scenic peninsula, and traditional architecture.
-
C.
Kékes
Kékes is the highest peak in Hungary, known for its popular hiking trails and ski resort facilities.
-
D.
Kazincbarcika
Kazincbarcika is an industrial town in northeastern Hungary, located in Borsod-Abaúj-Zemplén County.
-
E.
Villány
Villány is a small town in southern Hungary renowned as one of the country’s premier wine regions, especially famous for its red wines.
- 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_69d886d15af4819092f92f8a129763e6 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3f404f0e88190b7ac9ac523fdc7da |
completed | April 18, 2026, 9:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01483158348190abb96b36caaf455a |
completed | May 11, 2026, 3:08 a.m. |
Created at: April 10, 2026, 5:36 a.m.