Triple
T4274087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Zug |
E97004
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object | Zug |
E187177
|
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: Zug | Statement: [Lake Zug, nearCity, Zug]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zug Context triple: [Lake Zug, nearCity, Zug]
-
A.
Zug
chosen
Zug is a small, affluent Swiss city and canton known for its low taxes, picturesque lakeside setting, and role as a hub for international businesses and cryptocurrency companies.
-
B.
Olten
Olten is a town in the canton of Solothurn in northwestern Switzerland, known as an important railway junction and regional economic center.
-
C.
Kloten
Kloten is a town in the canton of Zurich in northern Switzerland, best known as the home of Zurich Airport.
-
D.
Zurich
Zurich is the largest city in Switzerland, known as a global financial hub and cultural center situated on the shores of Lake Zurich.
-
E.
Schaffhausen
Schaffhausen is a historic town and capital of the canton of the same name in northern Switzerland, known for its well-preserved medieval old town and proximity to the Rhine Falls.
- 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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3501abb74819086b2f04ac7a5c114 |
completed | March 12, 2026, 11:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5d069a3c08190abbbf4f163c31054 |
completed | March 14, 2026, 9:17 p.m. |
Created at: March 12, 2026, 11:07 p.m.