Triple

T16825109
Position Surface form Disambiguated ID Type / Status
Subject Stadt Nürnberg E408998 entity
Predicate locatedIn P40 FINISHED
Object Middle Franconia E17540 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: Middle Franconia | Statement: [Stadt Nürnberg, locatedIn, Middle Franconia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Middle Franconia
Context triple: [Stadt Nürnberg, locatedIn, Middle Franconia]
  • A. Middle Franconia chosen
    Middle Franconia is an administrative region in the German state of Bavaria, known for cities such as Nuremberg, Erlangen, and Fürth.
  • B. Lower Franconia
    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.
  • C. 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.
  • D. Western Palatinate
    Western Palatinate is a region in southwestern Germany that forms the western part of the Palatinate area in the state of Rhineland-Palatinate.
  • E. Middle Hesse
    Middle Hesse is a central region of the German state of Hesse known for its mix of historic university towns, industrial centers, and rural landscapes.
  • 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_69d88394566c8190b3dcbdc72935f7fa completed April 10, 2026, 4:59 a.m.
NER Named-entity recognition batch_69e3b310ffec81908087e5aaacc4a7c2 completed April 18, 2026, 4:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00b29e48f881908489bd77a9caec97 completed May 10, 2026, 4:30 p.m.
Created at: April 10, 2026, 5:23 a.m.