Triple
T1779475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kingdom of Bavaria |
E39255
|
entity |
| Predicate | rankInGermanEmpireByArea |
P32315
|
FINISHED |
| Object | second largest state after Prussia |
—
|
LITERAL 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: second largest state after Prussia | Statement: [Kingdom of Bavaria, rankInGermanEmpireByArea, second largest state after Prussia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankInGermanEmpireByArea Context triple: [Kingdom of Bavaria, rankInGermanEmpireByArea, second largest state after Prussia]
-
A.
areaOfMemberStatesApprox
Indicates the approximate total geographic area collectively covered by the member states of a given organization or grouping.
-
B.
rankInWorldByArea
Indicates the position of an entity in a global ordering based on its total area size.
-
C.
areaRankingInEurope
Indicates the position of an entity in a size-based ranking of areas within Europe.
-
D.
continentRankByArea
Indicates the relative position of a continent in an ordered list based on its total land area.
-
E.
rankInBritishIslesByArea
Indicates the position of an entity in an ordered list of areas specifically within the British Isles, based on its size relative to others.
- F. None of above. chosen
Provenance (4 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_69a88630519c8190a17addd83c4a3ef4 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab74dc9d1481908084ef07872a71f8 |
completed | March 7, 2026, 12:44 a.m. |
| PD | Predicate disambiguation | batch_69aa61cf3ca881908641fd73ce2f7c9d |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab74db3dbc8190ab256a4e158062b8 |
completed | March 7, 2026, 12:44 a.m. |
Created at: March 4, 2026, 7:31 p.m.