Triple
T15815444
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sihlsee |
E383463
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Willerzell
Willerzell is a small Swiss village in the canton of Schwyz, known for its scenic alpine setting and proximity to the Sihlsee reservoir.
|
E1178365
|
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: Willerzell | Statement: [Sihlsee, locatedNear, Willerzell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Willerzell Context triple: [Sihlsee, locatedNear, Willerzell]
-
A.
Hofstadt
Hofstadt is the maiden surname of Betty Draper, a central character on the television series "Mad Men."
-
B.
Bergneustadt
Bergneustadt is a small town in North Rhine-Westphalia, Germany, known for its location in the hilly Oberbergischer Kreis region and its traditional half-timbered architecture.
-
C.
Nordhoff
Nordhoff is a surname most notably associated with American author and journalist Charles Nordhoff.
-
D.
Waldstadt
Waldstadt is a district of Karlsruhe in the German state of Baden-Württemberg, characterized by its forested setting and primarily residential layout.
-
E.
Haselbach
Haselbach is a small municipality in the Straubing-Bogen district of Lower Bavaria in southeastern 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: Willerzell Triple: [Sihlsee, locatedNear, Willerzell]
Generated description
Willerzell is a small Swiss village in the canton of Schwyz, known for its scenic alpine setting and proximity to the Sihlsee reservoir.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Willerzell Target entity description: Willerzell is a small Swiss village in the canton of Schwyz, known for its scenic alpine setting and proximity to the Sihlsee reservoir.
-
A.
Hofstadt
Hofstadt is the maiden surname of Betty Draper, a central character on the television series "Mad Men."
-
B.
Bergneustadt
Bergneustadt is a small town in North Rhine-Westphalia, Germany, known for its location in the hilly Oberbergischer Kreis region and its traditional half-timbered architecture.
-
C.
Nordhoff
Nordhoff is a surname most notably associated with American author and journalist Charles Nordhoff.
-
D.
Waldstadt
Waldstadt is a district of Karlsruhe in the German state of Baden-Württemberg, characterized by its forested setting and primarily residential layout.
-
E.
Haselbach
Haselbach is a small municipality in the Straubing-Bogen district of Lower Bavaria in southeastern 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_69d86da2858c819090cc8481e7207b6e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0c4a219508190b8588120ec415ac7 |
completed | April 16, 2026, 11:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff99959f048190ae24a072387ec233 |
completed | May 9, 2026, 8:31 p.m. |
| NEDg | Description generation | batch_69ff9ad6b29081909ff2abb2c4d866a4 |
completed | May 9, 2026, 8:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff9b443280819088dbf18f7c57406b |
completed | May 9, 2026, 8:38 p.m. |
Created at: April 10, 2026, 4:49 a.m.