Triple
T15749823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Svitavy |
E381816
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object |
Langen
Langen is a town in Germany known for its residential character and proximity to major urban centers like Frankfurt am Main.
|
E1173586
|
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: Langen | Statement: [Svitavy, hasTwinTown, Langen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Langen Context triple: [Svitavy, hasTwinTown, Langen]
-
A.
Langeneß
Langeneß is a small Hallig island in the Wadden Sea off the coast of Schleswig-Holstein, Germany, known for its low-lying landscape, traditional Frisian culture, and vulnerability to tidal flooding.
-
B.
Lengerich
Lengerich is a town in North Rhine-Westphalia, Germany, known for its industrial heritage and location between Münster and Osnabrück.
-
C.
Langerfeld
Langerfeld is a district of the German city of Wuppertal, located in the state of North Rhine-Westphalia.
-
D.
Längenfeld
Längenfeld is a Tyrolean municipality in western Austria known for its alpine scenery and thermal spa resort Aqua Dome in the Ötztal valley.
-
E.
Leuenberg
Leuenberg is a village in Switzerland known as the site where major European Protestant churches concluded the Leuenberg Agreement on church fellowship.
- 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: Langen Triple: [Svitavy, hasTwinTown, Langen]
Generated description
Langen is a town in Germany known for its residential character and proximity to major urban centers like Frankfurt am Main.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Langen Target entity description: Langen is a town in Germany known for its residential character and proximity to major urban centers like Frankfurt am Main.
-
A.
Langeneß
Langeneß is a small Hallig island in the Wadden Sea off the coast of Schleswig-Holstein, Germany, known for its low-lying landscape, traditional Frisian culture, and vulnerability to tidal flooding.
-
B.
Lengerich
Lengerich is a town in North Rhine-Westphalia, Germany, known for its industrial heritage and location between Münster and Osnabrück.
-
C.
Langerfeld
Langerfeld is a district of the German city of Wuppertal, located in the state of North Rhine-Westphalia.
-
D.
Längenfeld
Längenfeld is a Tyrolean municipality in western Austria known for its alpine scenery and thermal spa resort Aqua Dome in the Ötztal valley.
-
E.
Leuenberg
Leuenberg is a village in Switzerland known as the site where major European Protestant churches concluded the Leuenberg Agreement on church fellowship.
- 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0502fd3608190b42e647b9c2b41a1 |
completed | April 16, 2026, 2:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff830b85408190b9ae4d6752524b99 |
completed | May 9, 2026, 6:55 p.m. |
| NEDg | Description generation | batch_69ff8388b3588190ae55c123bb19cb2c |
completed | May 9, 2026, 6:57 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff84125e808190a4d465d9effad639 |
completed | May 9, 2026, 6:59 p.m. |
Created at: April 10, 2026, 4:46 a.m.