Triple
T349972
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hamburg |
E7419
|
entity |
| Predicate | twinCity |
P1072
|
FINISHED |
| Object | St. Petersburg |
E916
|
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: St. Petersburg | Statement: [Hamburg, twinCity, St. Petersburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: St. Petersburg Context triple: [Hamburg, twinCity, St. Petersburg]
-
A.
St. Petersburg
chosen
St. Petersburg is a major Russian port city on the Baltic Sea, renowned for its imperial architecture, cultural heritage, and role as a historic capital of Russia.
-
B.
St. Petersburg, Florida
St. Petersburg, Florida is a coastal city on Florida’s Gulf Coast known for its sunny climate, beaches, and vibrant arts and cultural scene.
-
C.
Moscow
Moscow is the capital and largest city of Russia, serving as its political, economic, and cultural center.
-
D.
Tsentralny District of Saint Petersburg
Tsentralny District of Saint Petersburg is the historic and administrative heart of the city, encompassing its main cultural, governmental, and commercial centers.
-
E.
Canton
Canton is the historical Western name for Guangzhou, a major port city in southern China and the capital of Guangdong province.
- 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_69a2e7e696948190bebc966535995e45 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2eb1f028c819098fa6480b4ca5cf0 |
completed | Feb. 28, 2026, 1:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a49848877881909ace940c0325bc4a |
completed | March 1, 2026, 7:49 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.