Triple

T15250624
Position Surface form Disambiguated ID Type / Status
Subject Meissen district E364508 entity
Predicate capital P234 FINISHED
Object Meissen E74716 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: Meissen | Statement: [Meissen district, capital, Meissen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Meissen
Context triple: [Meissen district, capital, Meissen]
  • A. Meissen chosen
    Meissen is a historic town in eastern Germany renowned for its medieval architecture and as the birthplace of European hard-paste porcelain.
  • B. Glashütten
    Glashütten is a small municipality in the Hochtaunus district of Hesse, Germany, known for its scenic location in the Taunus mountains and its residential, forested character.
  • C. Glashütte
    Glashütte is a renowned German town in Saxony famous worldwide as a historic center of high-end mechanical watchmaking.
  • D. Glashütte
    Glashütte is a former municipality in northern Germany that was incorporated into the town of Norderstedt.
  • E. Meissen district
    Meissen district is an administrative district in the Free State of Saxony in eastern Germany, known for its historic towns, wine-growing areas, and proximity to the city of Dresden.
  • 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_69d85a0dde7481908fc64d1e82d5d20d completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e007f62b9c8190b9ad40e2d1912b63 completed April 15, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69fee5f184d481909eb4294ee3648226 completed May 9, 2026, 7:44 a.m.
Created at: April 10, 2026, 3:13 a.m.