Triple
T6179840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M6 beam line |
E137913
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Meyrin |
E47537
|
NE FINISHED |
How this triple was built (2 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: Meyrin | Statement: [M6 beam line, locatedNear, Meyrin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meyrin Context triple: [M6 beam line, locatedNear, Meyrin]
-
A.
Meyrin
chosen
Meyrin is a municipality in the canton of Geneva, Switzerland, best known for hosting major CERN facilities including the Super Proton Synchrotron.
-
B.
Saanen
Saanen is a picturesque Swiss village in the Bernese Oberland known for its traditional chalets, alpine scenery, and proximity to the upscale resort town of Gstaad.
-
C.
Ruetz
Ruetz is a river in the Stubai Valley of Tyrol, Austria, known for its alpine course through the Stubai Alps before joining the Sill River.
-
D.
Saas-Fee
Saas-Fee is a high-altitude Swiss alpine village and ski resort in the Valais Alps, known for its car-free center, extensive glacier skiing, and dramatic mountain scenery.
-
E.
Thun
Thun is a historic Swiss town in the canton of Bern, known for its medieval old town, lakeside setting on Lake Thun, and views of the surrounding Alps.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c008a80f748190ba3d07ffc81acb29 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05dcb77948190b5385438f81bf0a8 |
completed | March 22, 2026, 9:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c20d7fbbcc8190902e8f104d9991de |
completed | March 24, 2026, 4:05 a.m. |
Created at: March 22, 2026, 4:18 p.m.