Triple
T5608892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leonding |
E147302
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object |
Wilhering
Wilhering is a municipality in Upper Austria, known for the historic Wilhering Abbey and its location near the city of Linz.
|
E536985
|
NE FINISHED |
How this triple was built (4 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: Wilhering | Statement: [Leonding, adjacentTo, Wilhering]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wilhering Context triple: [Leonding, adjacentTo, Wilhering]
-
A.
Grafenwerth
Grafenwerth is a Rhine River island in Germany known as a recreational area with parks, swimming facilities, and scenic views near the town of Bad Honnef.
-
B.
Kelheim
Kelheim is a historic town in southeastern Germany known for its location at the confluence of the Danube and Altmühl rivers and its proximity to the Danube Gorge and the Befreiungshalle monument.
-
C.
Hornsberg
Hornsberg is a waterfront residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
-
D.
Wiehe
Wiehe is a small town in the German state of Thuringia, historically notable as the birthplace of the influential 19th-century historian Leopold von Ranke.
-
E.
Wiesen
Wiesen is a small locality that forms one of the subdivisions of the town of Lichtenfels in Germany.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Wilhering Triple: [Leonding, adjacentTo, Wilhering]
Generated description
Wilhering is a municipality in Upper Austria, known for the historic Wilhering Abbey and its location near the city of Linz.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wilhering Target entity description: Wilhering is a municipality in Upper Austria, known for the historic Wilhering Abbey and its location near the city of Linz.
-
A.
Grafenwerth
Grafenwerth is a Rhine River island in Germany known as a recreational area with parks, swimming facilities, and scenic views near the town of Bad Honnef.
-
B.
Kelheim
Kelheim is a historic town in southeastern Germany known for its location at the confluence of the Danube and Altmühl rivers and its proximity to the Danube Gorge and the Befreiungshalle monument.
-
C.
Hornsberg
Hornsberg is a waterfront residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
-
D.
Wiehe
Wiehe is a small town in the German state of Thuringia, historically notable as the birthplace of the influential 19th-century historian Leopold von Ranke.
-
E.
Wiesen
Wiesen is a small locality that forms one of the subdivisions of the town of Lichtenfels in Germany.
- F. None of above. chosen
Provenance (5 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_69c0090500f881908374285baf0ac46f |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c020fe7ee0819088ced51afd9a4f93 |
completed | March 22, 2026, 5:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c04d4876208190b8f5b5ab2ba762d8 |
completed | March 22, 2026, 8:12 p.m. |
| NEDg | Description generation | batch_69c04e88680c8190845723f52c060fb7 |
completed | March 22, 2026, 8:18 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c04f7b889c81909db7cb4baf40ed80 |
completed | March 22, 2026, 8:22 p.m. |
Created at: March 22, 2026, 3:39 p.m.