Triple
T21579491
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maximilian Benedikt |
E532486
|
entity |
| Predicate | placeOfActivity |
P1527
|
FINISHED |
| Object | Vienna |
—
|
NE NERFINISHED |
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: [Maximilian Benedikt, placeOfActivity, Vienna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vienna Context triple: [Maximilian Benedikt, placeOfActivity, 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 ancient Roman name for the city of Vienne in southeastern France, which was an important Roman settlement and administrative center in Gaul.
-
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.
Vienna
Vienna is the strong-willed saloon owner and central female protagonist in the 1954 Western film "Johnny Guitar."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c4618bec8190bcb0feb74568cbb1 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eeeb5b8118819095fd751a03fbe8e8 |
completed | April 27, 2026, 4:51 a.m. |
Created at: April 16, 2026, 6:31 p.m.