Triple
T4658677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ötztal |
E102473
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Umhausen
Umhausen is a municipality in the Tyrolean Alps of western Austria, known for its scenic mountain setting and proximity to popular hiking and skiing areas.
|
E457443
|
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: Umhausen | Statement: [Ötztal, contains, Umhausen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Umhausen Context triple: [Ötztal, contains, Umhausen]
-
A.
Gerswalde
Gerswalde is a small rural municipality in the Uckermark district of Brandenburg, northeastern Germany.
-
B.
Creutzwald
Creutzwald is a small industrial town in northeastern France near the German border, known historically for its coal mining and glassmaking industries.
-
C.
Haldenstein
Haldenstein is a small Swiss village in the canton of Graubünden, known in architecture circles as the longtime base of renowned architect Peter Zumthor.
-
D.
Marienberg
Marienberg is a historic mining town in Saxony, Germany, known for its Renaissance-era planned layout and location in the central Ore Mountains.
-
E.
Haller
Haller is a surname most notably associated with Ernest Haller, an American cinematographer renowned for his work in classic Hollywood films.
- 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: Umhausen Triple: [Ötztal, contains, Umhausen]
Generated description
Umhausen is a municipality in the Tyrolean Alps of western Austria, known for its scenic mountain setting and proximity to popular hiking and skiing areas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Umhausen Target entity description: Umhausen is a municipality in the Tyrolean Alps of western Austria, known for its scenic mountain setting and proximity to popular hiking and skiing areas.
-
A.
Gerswalde
Gerswalde is a small rural municipality in the Uckermark district of Brandenburg, northeastern Germany.
-
B.
Creutzwald
Creutzwald is a small industrial town in northeastern France near the German border, known historically for its coal mining and glassmaking industries.
-
C.
Haldenstein
Haldenstein is a small Swiss village in the canton of Graubünden, known in architecture circles as the longtime base of renowned architect Peter Zumthor.
-
D.
Marienberg
Marienberg is a historic mining town in Saxony, Germany, known for its Renaissance-era planned layout and location in the central Ore Mountains.
-
E.
Haller
Haller is a surname most notably associated with Ernest Haller, an American cinematographer renowned for his work in classic Hollywood films.
- 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_69bd43d823288190952279faa0d1d066 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd63271a548190bd9662b69a45d9a5 |
completed | March 20, 2026, 3:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdfaf5a0988190b097ef71301aebbe |
completed | March 21, 2026, 1:57 a.m. |
| NEDg | Description generation | batch_69bdfc0964c881909e6b98a1c8ea747f |
completed | March 21, 2026, 2:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bdfce1be788190ae3418df301e5136 |
completed | March 21, 2026, 2:05 a.m. |
Created at: March 20, 2026, 1:15 p.m.