Triple

T1193520
Position Surface form Disambiguated ID Type / Status
Subject Fontainebleau E25615 entity
Predicate twinTown P1072 FINISHED
Object Konstanz E237005 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: Konstanz | Statement: [Fontainebleau, twinTown, Konstanz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Konstanz
Context triple: [Fontainebleau, twinTown, Konstanz]
  • A. Konstanz chosen
    Konstanz is a historic city on the shores of Lake Constance in southern Germany, known for its well-preserved medieval old town and role as a regional cultural and economic center.
  • B. Augsburg
    Augsburg is one of Germany’s oldest cities, a historic Bavarian center known for its rich Renaissance heritage and role as a major medieval trading hub.
  • C. Regensburg
    Regensburg is a historic city in southeastern Germany known for its well-preserved medieval old town on the Danube River.
  • D. Ulm
    Ulm is a historic city in the German state of Baden-Württemberg, best known for its towering Gothic cathedral and as the birthplace of physicist Albert Einstein.
  • E. Leonberg
    Leonberg is a historic town in the German state of Baden-Württemberg, near Stuttgart, known for its picturesque old town and Renaissance-era Pomeranzengarten (orange garden).
  • 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_69a49429f5ec8190a6a205eb0ae81e5e completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bd7743548190a70d3f3c7378aaa7 completed March 1, 2026, 10:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69ae58a410a8819091c9bc3f46b0c09d completed March 9, 2026, 5:20 a.m.
Created at: March 1, 2026, 7:46 p.m.