Triple
T17098808
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Autobahn A3 |
E414920
|
entity |
| Predicate | connectsRegion |
P845
|
FINISHED |
| Object | Lower Franconia |
E47272
|
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: Lower Franconia | Statement: [Autobahn A3, connectsRegion, Lower Franconia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lower Franconia Context triple: [Autobahn A3, connectsRegion, Lower Franconia]
-
A.
Lower Franconia
chosen
Lower Franconia is an administrative region in northwestern Bavaria, Germany, known for its historic cities like Würzburg and its prominent wine-growing areas along the Main River.
-
B.
Upper Franconia
Upper Franconia is a region in northern Bavaria, Germany, known for its historic towns, dense concentration of breweries, and rich Franconian cultural heritage.
-
C.
Middle Franconia
Middle Franconia is an administrative region in the German state of Bavaria, known for cities such as Nuremberg, Erlangen, and Fürth.
-
D.
Lower Bavaria
Lower Bavaria is an administrative region in southeastern Germany known for its rural landscapes, historic towns, and location along the Danube River.
-
E.
Mittelfranken
Mittelfranken is a region in the German state of Bavaria known for its rich cultural traditions, historic cities, and significant contributions to the state's intangible cultural heritage.
- 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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbffafb08190baf9e0b4fdf1b404 |
completed | April 18, 2026, 7:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01482597e88190859b35b1c4aa8e84 |
completed | May 11, 2026, 3:08 a.m. |
Created at: April 10, 2026, 5:35 a.m.