Triple
T3822972
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sezimovo Ústí |
E88618
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object |
Waldsassen
Waldsassen is a small town in Bavaria, Germany, known for its historic Cistercian abbey and richly decorated Baroque basilica.
|
E411703
|
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: Waldsassen | Statement: [Sezimovo Ústí, hasTwinTown, Waldsassen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Waldsassen Context triple: [Sezimovo Ústí, hasTwinTown, Waldsassen]
-
A.
Schwandorf
Schwandorf is a town in the Upper Palatinate region of Bavaria, Germany, known as a local administrative and commercial center on the Naab River.
-
B.
Mittenwald
Mittenwald is a picturesque Bavarian town renowned for its traditional violin-making heritage and scenic setting in the German Alps near the Austrian border.
-
C.
Altötting
Altötting is a Bavarian pilgrimage town renowned as one of Germany’s most important Catholic shrines, centered around the Chapel of Grace and its venerated Black Madonna.
-
D.
Tirschenreuth
Tirschenreuth is a town in northeastern Bavaria, Germany, known for its historic town center and surrounding lake and pond landscapes.
-
E.
Oberwallenstadt
Oberwallenstadt is a village and district of the town of Lichtenfels in the Upper Franconia region of Bavaria, 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: Waldsassen Triple: [Sezimovo Ústí, hasTwinTown, Waldsassen]
Generated description
Waldsassen is a small town in Bavaria, Germany, known for its historic Cistercian abbey and richly decorated Baroque basilica.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Waldsassen Target entity description: Waldsassen is a small town in Bavaria, Germany, known for its historic Cistercian abbey and richly decorated Baroque basilica.
-
A.
Schwandorf
Schwandorf is a town in the Upper Palatinate region of Bavaria, Germany, known as a local administrative and commercial center on the Naab River.
-
B.
Mittenwald
Mittenwald is a picturesque Bavarian town renowned for its traditional violin-making heritage and scenic setting in the German Alps near the Austrian border.
-
C.
Altötting
Altötting is a Bavarian pilgrimage town renowned as one of Germany’s most important Catholic shrines, centered around the Chapel of Grace and its venerated Black Madonna.
-
D.
Tirschenreuth
Tirschenreuth is a town in northeastern Bavaria, Germany, known for its historic town center and surrounding lake and pond landscapes.
-
E.
Oberwallenstadt
Oberwallenstadt is a village and district of the town of Lichtenfels in the Upper Franconia region of Bavaria, 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_69aed9538cf881909d9ce8ca4ac7c18c |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeea63fe2c8190825f6e9451f6aa50 |
completed | March 9, 2026, 3:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5627ebbc48190914b663bab5e2a82 |
completed | March 14, 2026, 1:28 p.m. |
| NEDg | Description generation | batch_69b563b3db0481909f3dd2a9e6a88e6e |
completed | March 14, 2026, 1:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b567e223cc8190aa1d7e827e6c70fd |
completed | March 14, 2026, 1:51 p.m. |
Created at: March 9, 2026, 3:17 p.m.