Triple
T5853000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wernigerode |
E130080
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Krems an der Donau |
E238677
|
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: Krems an der Donau | Statement: [Wernigerode, hasTwinTown, Krems an der Donau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Krems an der Donau Context triple: [Wernigerode, hasTwinTown, Krems an der Donau]
-
A.
Krems an der Donau
chosen
Krems an der Donau is a historic Austrian city on the Danube River, renowned for its well-preserved medieval old town and its role as a gateway to the Wachau wine region.
-
B.
Tulln an der Donau
Tulln an der Donau is an Austrian town on the Danube River, known for its rich history and as the birthplace of painter Egon Schiele.
-
C.
Gmunden
Gmunden is a picturesque town in Upper Austria known for its lakeside setting on the Traunsee and its historic ceramics industry.
-
D.
Vöcklabruck
Vöcklabruck is a small historic town in Upper Austria known as a regional center near the Attersee lake and the foothills of the Alps.
-
E.
Leoben
Leoben is a historic industrial and university city in the Austrian state of Styria, known especially for its steel industry and mining university.
- 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_69c0084de39081909eb34e6bed74215a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0355038008190bf38980349b533e2 |
completed | March 22, 2026, 6:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c13540fa788190a09a509267bdb147 |
completed | March 23, 2026, 12:42 p.m. |
Created at: March 22, 2026, 3:55 p.m.