Triple

T16975217
Position Surface form Disambiguated ID Type / Status
Subject Bamberg Bahnhof E411791 entity
Predicate connectsTo P845 FINISHED
Object Lichtenfels E11679 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: Lichtenfels | Statement: [Bamberg Bahnhof, connectsTo, Lichtenfels]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lichtenfels
Context triple: [Bamberg Bahnhof, connectsTo, Lichtenfels]
  • A. Lichtenfels chosen
    Lichtenfels is a town in the Upper Franconia region of Bavaria, Germany, known for its basket-making tradition and historic architecture.
  • B. Lichtenfels
    Lichtenfels is a small town in the Waldeck-Frankenberg district of northern Hesse, Germany, known for its rural setting and proximity to the Edersee and Kellerwald-Edersee National Park.
  • C. Lülsfeld
    Lülsfeld is a small municipality in the Schweinfurt district of Lower Franconia in northern Bavaria, Germany.
  • D. Altenstadt
    Altenstadt is a small Bavarian municipality in southern Germany, situated within the rural district of Weilheim-Schongau.
  • E. Lauterhofen
    Lauterhofen is a market town in Bavaria, Germany, known for its rural character and location within the Upper Palatinate region.
  • 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_69d886ca8f348190812768ea8d5055ce completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d18300c8819080c8bf19962754ba completed April 18, 2026, 6:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0148201f7c8190a964723ca7ef2b68 completed May 11, 2026, 3:08 a.m.
Created at: April 10, 2026, 5:31 a.m.