Triple
T228703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lower Saxony |
E4364
|
entity |
| Predicate | bordersState |
P224
|
FINISHED |
| Object | Hesse |
E14304
|
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: Hesse | Statement: [Lower Saxony, bordersState, Hesse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hesse Context triple: [Lower Saxony, bordersState, Hesse]
-
A.
Hesse
chosen
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.
-
B.
Bavaria
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.
-
C.
Franconia
Franconia is a suburban community in Fairfax County, Northern Virginia, known for its residential neighborhoods and proximity to Washington, D.C.
-
D.
Harz
Harz is a low mountain range in central Germany known for its dense forests, mining history, and association with German folklore such as the Brocken and Walpurgis Night.
-
E.
Saarland
Saarland is a small federal state in southwestern Germany known for its industrial history, Franco-German cultural influences, and location along the borders with France and Luxembourg.
- 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_69a257363ffc81909757bde7ab3404da |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25c9140c48190b90647400854b37e |
completed | Feb. 28, 2026, 3:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a405ef40248190b81d461f3b6d4baa |
completed | March 1, 2026, 9:25 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.