Triple
T4283039
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samuel van Hoogstraten |
E97198
|
entity |
| Predicate | workLocation |
P7
|
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: [Samuel van Hoogstraten, workLocation, Vienna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vienna Context triple: [Samuel van Hoogstraten, workLocation, 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 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.
-
D.
Vienne
Vienne is a historic town in southeastern France known for its well-preserved Roman and medieval heritage, including ancient temples, a Roman theater, and a Gothic cathedral.
-
E.
Vienne
Vienne is a major river in west-central France that flows through the Limousin region before joining the Loire.
- 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_69b3454595848190a0e6bbb6a2bea040 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3503a84548190989a96d1a30d6ef7 |
completed | March 12, 2026, 11:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5b766915c81909b778ce391e43c22 |
completed | March 14, 2026, 7:30 p.m. |
Created at: March 12, 2026, 11:07 p.m.