Triple
T9826875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krems an der Donau |
E238677
|
entity |
| Predicate | hasLandmark |
P105
|
FINISHED |
| Object |
Steiner Tor
Steiner Tor is a historic city gate and iconic symbol of Krems an der Donau in Lower Austria, dating back to the medieval fortifications of the town.
|
E822990
|
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: Steiner Tor | Statement: [Krems an der Donau, hasLandmark, Steiner Tor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Steiner Tor Context triple: [Krems an der Donau, hasLandmark, Steiner Tor]
-
A.
Syrgenstein
Syrgenstein is a small municipality in the Heidenheim district of the German state of Baden-Württemberg.
-
B.
Striegistal
Striegistal is a municipality in the district of Mittelsachsen in the German state of Saxony, known for its rural landscape and small villages.
-
C.
Störnstein
Störnstein is a small municipality in the Upper Palatinate region of Bavaria, Germany.
-
D.
Nebelstein
Nebelstein is a prominent mountain in Lower Austria known as the highest elevation in the Waldviertel region and a popular destination for hiking and nature tourism.
-
E.
Steiner
Steiner is a common German-language surname borne by numerous notable individuals across fields such as music, philosophy, and science.
- 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: Steiner Tor Triple: [Krems an der Donau, hasLandmark, Steiner Tor]
Generated description
Steiner Tor is a historic city gate and iconic symbol of Krems an der Donau in Lower Austria, dating back to the medieval fortifications of the town.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Steiner Tor Target entity description: Steiner Tor is a historic city gate and iconic symbol of Krems an der Donau in Lower Austria, dating back to the medieval fortifications of the town.
-
A.
Syrgenstein
Syrgenstein is a small municipality in the Heidenheim district of the German state of Baden-Württemberg.
-
B.
Striegistal
Striegistal is a municipality in the district of Mittelsachsen in the German state of Saxony, known for its rural landscape and small villages.
-
C.
Störnstein
Störnstein is a small municipality in the Upper Palatinate region of Bavaria, Germany.
-
D.
Nebelstein
Nebelstein is a prominent mountain in Lower Austria known as the highest elevation in the Waldviertel region and a popular destination for hiking and nature tourism.
-
E.
Steiner
Steiner is a common German-language surname borne by numerous notable individuals across fields such as music, philosophy, and science.
- 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_69ca84e0dd1881909800765d1e21f735 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb324e7848190b9424a78ca653afe |
completed | April 2, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1cc88a86c819088f259a049eec4db |
completed | April 5, 2026, 2:44 a.m. |
| NEDg | Description generation | batch_69d1cdba64d08190bf0b83d419c4461b |
completed | April 5, 2026, 2:49 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1ce526a2c819098b103ad83c19445 |
completed | April 5, 2026, 2:52 a.m. |
Created at: March 30, 2026, 8:32 p.m.