Triple
T17367910
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Visp District |
E422231
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Staldenried
Staldenried is a small Swiss mountain municipality in the canton of Valais, known for its alpine scenery and traditional rural character.
|
E1271535
|
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: Staldenried | Statement: [Visp District, contains, Staldenried]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Staldenried Context triple: [Visp District, contains, Staldenried]
-
A.
Biebelried
Biebelried is a small municipality in the Kitzingen district of Bavaria, Germany, known for its rural character and proximity to the Franconian wine region.
-
B.
Pleiskirchen
Pleiskirchen is a small municipality in Bavaria, Germany, known as the birthplace of German Catholic priest and musician Georg Ratzinger, brother of Pope Benedict XVI.
-
C.
Steinlach
Steinlach is a small river in the German state of Baden-Württemberg that flows through the city of Tübingen before joining the Neckar.
-
D.
Adelsried
Adelsried is a small municipality in the Swabian region of Bavaria in southern Germany.
-
E.
Neulengbach
Neulengbach is a small town in Lower Austria known for its historic center and its location within the Vienna Woods region.
- 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: Staldenried Triple: [Visp District, contains, Staldenried]
Generated description
Staldenried is a small Swiss mountain municipality in the canton of Valais, known for its alpine scenery and traditional rural character.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Staldenried Target entity description: Staldenried is a small Swiss mountain municipality in the canton of Valais, known for its alpine scenery and traditional rural character.
-
A.
Biebelried
Biebelried is a small municipality in the Kitzingen district of Bavaria, Germany, known for its rural character and proximity to the Franconian wine region.
-
B.
Pleiskirchen
Pleiskirchen is a small municipality in Bavaria, Germany, known as the birthplace of German Catholic priest and musician Georg Ratzinger, brother of Pope Benedict XVI.
-
C.
Steinlach
Steinlach is a small river in the German state of Baden-Württemberg that flows through the city of Tübingen before joining the Neckar.
-
D.
Adelsried
Adelsried is a small municipality in the Swabian region of Bavaria in southern Germany.
-
E.
Neulengbach
Neulengbach is a small town in Lower Austria known for its historic center and its location within the Vienna Woods region.
- 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_69d889d6535c81908be333c01deaec4e |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a661fc08190a4c386125bddb16b |
completed | April 19, 2026, 2:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01c1eb500c8190ab3f9d3cbd7705c7 |
completed | May 11, 2026, 11:47 a.m. |
| NEDg | Description generation | batch_6a01c2aa19408190840547b60786cc18 |
completed | May 11, 2026, 11:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01c35d05388190ade7044202781e20 |
completed | May 11, 2026, 11:54 a.m. |
Created at: April 10, 2026, 5:44 a.m.