Triple
T16071633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris–Zurich high-speed service |
E389876
|
entity |
| Predicate | terminus |
P388
|
FINISHED |
| Object | Zurich |
E13407
|
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: Zurich | Statement: [Paris–Zurich high-speed service, terminus, Zurich]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zurich Context triple: [Paris–Zurich high-speed service, terminus, Zurich]
-
A.
Zurich
chosen
Zurich is the largest city in Switzerland, known as a global financial hub and cultural center situated on the shores of Lake Zurich.
-
B.
Stettlen
Stettlen is a municipality in the canton of Bern in Switzerland, situated just east of the city of Bern and functioning largely as a residential and commuter community.
-
C.
Berne
Berne is the de facto capital city of Switzerland and the seat of its federal government institutions.
-
D.
Geneva
Geneva is a major Swiss city on Lake Geneva known for hosting numerous international organizations, including United Nations agencies and the Red Cross.
-
E.
Geneva
Geneva is a small city in New York's Finger Lakes region, known for its lakeside setting on Seneca Lake and its historic colleges and wineries.
- 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_69d86daf32ec8190a8c0466c8f49c3c0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e183be909c8190ac6c37ab047151ae |
completed | April 17, 2026, 12:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a004f372f7c8190ba04b8bd13bff95c |
completed | May 10, 2026, 9:26 a.m. |
Created at: April 10, 2026, 4:57 a.m.