Triple
T14868066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Limmatquai |
E349668
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Central (Zürich) |
E1027083
|
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: Central (Zürich) | Statement: [Limmatquai, connectsTo, Central (Zürich)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Central (Zürich) Context triple: [Limmatquai, connectsTo, Central (Zürich)]
-
A.
central Zurich
chosen
Central Zurich is the bustling core area of Zurich, Switzerland, known for its dense urban fabric, major cultural institutions, shopping streets, and key business and transport hubs.
-
B.
Zurich Wiedikon
Zurich Wiedikon is a residential and commercial district in the city of Zurich, Switzerland, known for its urban character, good public transport connections, and proximity to the Sihl River.
-
C.
Kloten
Kloten is a town in the canton of Zurich in northern Switzerland, best known as the home of Zurich Airport.
-
D.
Dübendorf
Dübendorf is a municipality in the canton of Zurich, Switzerland, known for its proximity to Zurich and its historic military and aviation facilities.
-
E.
Zug
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.
- 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_69d822ee4f408190b6ac3b2fa434f0df |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5776b848190bfe3a06ff261dc31 |
completed | April 15, 2026, 12:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe651067cc8190b9c218ef1f802762 |
completed | May 8, 2026, 10:34 p.m. |
Created at: April 10, 2026, 1:55 a.m.