Triple
T21000399
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lüterkofen-Ichertswil |
E517269
|
entity |
| Predicate | neighboringMunicipality |
P17964
|
FINISHED |
| Object |
Messen
Messen is a municipality in the canton of Solothurn in Switzerland, known for its rural character and location in the Bucheggberg region.
|
E1461994
|
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: Messen | Statement: [Lüterkofen-Ichertswil, neighboringMunicipality, Messen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Messen Context triple: [Lüterkofen-Ichertswil, neighboringMunicipality, Messen]
-
A.
Messe
Messe is a Nuremberg U-Bahn station serving the city’s exhibition and trade fair grounds.
-
B.
Toki Messe
Toki Messe is a large convention and exhibition center in Niigata, Japan, known for hosting major events, trade shows, and cultural festivals.
-
C.
The Expo
The Expo is a well-known multipurpose event and exhibition venue in Portland, Oregon, hosting trade shows, conventions, and community events.
-
D.
Expo
Expo is an open-source platform and toolchain for building, deploying, and iterating on React Native applications.
-
E.
Expo
Expo is a popular brand best known for its dry-erase markers and related whiteboard accessories commonly used in schools, offices, and homes.
- 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: Messen Triple: [Lüterkofen-Ichertswil, neighboringMunicipality, Messen]
Generated description
Messen is a municipality in the canton of Solothurn in Switzerland, known for its rural character and location in the Bucheggberg region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Messen Target entity description: Messen is a municipality in the canton of Solothurn in Switzerland, known for its rural character and location in the Bucheggberg region.
-
A.
Messe
Messe is a Nuremberg U-Bahn station serving the city’s exhibition and trade fair grounds.
-
B.
Toki Messe
Toki Messe is a large convention and exhibition center in Niigata, Japan, known for hosting major events, trade shows, and cultural festivals.
-
C.
The Expo
The Expo is a well-known multipurpose event and exhibition venue in Portland, Oregon, hosting trade shows, conventions, and community events.
-
D.
Expo
Expo is an open-source platform and toolchain for building, deploying, and iterating on React Native applications.
-
E.
Expo
Expo is a popular brand best known for its dry-erase markers and related whiteboard accessories commonly used in schools, offices, and homes.
- 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_69e0b5006e2881909fc2383f841740cc |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fc24a6dc8190a6bf81cf1d9590c0 |
completed | April 21, 2026, 4:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a093b545f5081909152211b7e03bea1 |
completed | May 17, 2026, 3:51 a.m. |
| NEDg | Description generation | batch_6a093e5b743881908dea54f627f2a220 |
completed | May 17, 2026, 4:04 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a093ebe37cc8190961b02f8f12ce4b0 |
completed | May 17, 2026, 4:06 a.m. |
Created at: April 16, 2026, 1:52 p.m.