Triple
T104086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grand Alliance |
E2102
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object | Bavaria |
E7752
|
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: Bavaria | Statement: [Grand Alliance, hasMember, Bavaria]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bavaria Context triple: [Grand Alliance, hasMember, Bavaria]
-
A.
Bavaria
chosen
Bavaria is a historic region and federal state in southeastern Germany, known for its distinct cultural traditions, large size and population, and major cities such as Munich.
-
B.
Saxony
Saxony is a historic region and former kingdom in eastern Germany, known for its cultural centers like Dresden and Leipzig and its significant role in Central European history.
-
C.
Baden-Württemberg
Baden-Württemberg is a federal state in southwest Germany known for its strong economy, automotive industry, and cities like Stuttgart, Heidelberg, and Freiburg.
-
D.
Franconia
Franconia is a suburban community in Fairfax County, Northern Virginia, known for its residential neighborhoods and proximity to Washington, D.C.
-
E.
Hesse
Hesse is a federal state in central Germany known for its financial hub Frankfurt am Main and its mix of urban centers, forests, and historic towns.
- 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_69a24e0a5b7c81908d52da08c60dabc4 |
completed | Feb. 28, 2026, 2:08 a.m. |
| NER | Named-entity recognition | batch_69a256aaed108190ba12e36364d30ede |
completed | Feb. 28, 2026, 2:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a38b8959dc8190b6ec8ef2583505a1 |
completed | March 1, 2026, 12:42 a.m. |
Created at: Feb. 28, 2026, 2:12 a.m.