Triple
T8790264
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ranieri de’ Calzabigi |
E209143
|
entity |
| Predicate | workedIn |
P1527
|
FINISHED |
| Object | Vienna |
E7023
|
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: Vienna | Statement: [Ranieri de’ Calzabigi, workedIn, Vienna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vienna Context triple: [Ranieri de’ Calzabigi, workedIn, Vienna]
-
A.
Vienna
chosen
Vienna is the capital city of Austria, renowned for its rich imperial history, classical music heritage, and vibrant cultural and intellectual life.
-
B.
Vienna
Vienna is a small town in Dane County, Wisconsin, known for its rural character and proximity to the Madison metropolitan area.
-
C.
Vienna
Vienna is the strong-willed saloon owner and central female protagonist in the 1954 Western film "Johnny Guitar."
-
D.
Vienna
Vienna is a suburban town in Fairfax County, Virginia, known for its residential neighborhoods, proximity to Washington, D.C., and access to the Washington Metro via the nearby Vienna/Fairfax–GMU station.
-
E.
Wien
Wien is a German surname most notably borne by physicist Wilhelm Wien, known for his work on blackbody radiation and Wien's displacement law.
- 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_69ca836168108190bb43d3dc235c1f55 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5f8d25f881908863d636fa57a8a2 |
completed | March 31, 2026, 11:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf5174db7881908597d5dc472adde9 |
completed | April 3, 2026, 5:34 a.m. |
Created at: March 30, 2026, 6:43 p.m.