Triple
T17147940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aulendorf–Kißlegg railway |
E416142
|
entity |
| Predicate | startPoint |
P389
|
FINISHED |
| Object | Aulendorf |
—
|
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: Aulendorf | Statement: [Aulendorf–Kißlegg railway, startPoint, Aulendorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aulendorf Context triple: [Aulendorf–Kißlegg railway, startPoint, Aulendorf]
-
A.
Aulendorf
chosen
Aulendorf is a small town in the Upper Swabia region of southern Germany, known for its historic castle and spa facilities.
-
B.
Allendorf
Allendorf is a village-level subdivision of the town of Sundern in the Hochsauerland district of North Rhine-Westphalia, Germany.
-
C.
Aulhausen
Aulhausen is a district of the town Rüdesheim am Rhein in the Rheingau region of Hesse, Germany, known for its scenic vineyards and rural character.
-
D.
Arnsdorf
Arnsdorf is a small municipality in the German state of Saxony, known for its rural character and location near the city of Dresden.
-
E.
Nauendorf
Nauendorf is a village in the German state of Saxony-Anhalt that forms part of the town of Wettin-Löbejün.
- 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_69d886d15af4819092f92f8a129763e6 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3f404f0e88190b7ac9ac523fdc7da |
completed | April 18, 2026, 9:13 p.m. |
Created at: April 10, 2026, 5:36 a.m.