Triple
T613487
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Johannes Kepler |
E12150
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object | Linz |
E75219
|
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: Linz | Statement: [Johannes Kepler, workLocation, Linz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Linz Context triple: [Johannes Kepler, workLocation, Linz]
-
A.
Linz
chosen
Linz is a major Austrian city known for its industrial heritage, vibrant cultural scene, and location along the Danube River.
-
B.
Salzburg
Salzburg is a historic Austrian city on the Salzach River, renowned for its baroque architecture, Alpine setting, and as the birthplace of composer Wolfgang Amadeus Mozart.
-
C.
Vienna
Vienna is the capital city of Austria, renowned for its rich imperial history, classical music heritage, and vibrant cultural and intellectual life.
-
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.
Graz
Graz is Austria’s second-largest city, known for its well-preserved medieval old town and historic role as a center of science and education.
- 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_69a493309df48190a327f748e88049a6 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49e08dbf88190ab050078a63e266b |
completed | March 1, 2026, 8:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a6787173e08190bef6734294b60c13 |
completed | March 3, 2026, 5:58 a.m. |
Created at: March 1, 2026, 7:35 p.m.