Triple

T1862361
Position Surface form Disambiguated ID Type / Status
Subject Ems E34843 entity
Predicate flowsThrough P225 FINISHED
Object Hesse E14304 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: Hesse | Statement: [Ems, flowsThrough, Hesse]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hesse
Context triple: [Ems, flowsThrough, Hesse]
  • A. Hesse chosen
    Hesse is a federal state in central Germany known for its financial hub Frankfurt am Main and its mix of urban centers, forests, and historic towns.
  • B. South Hesse
    South Hesse is a region in the southern part of the German state of Hesse that includes major urban and economic centers such as Darmstadt and the Rhine-Main area.
  • C. Odenwald
    Odenwald is a low mountain range in southwestern Germany known for its forested hills, historic towns, and scenic hiking landscapes.
  • D. Bavaria
    Bavaria is a historic region and federal state in southeastern Germany, known for its distinct cultural traditions, large size and population, and major cities such as Munich.
  • E. North Hesse
    North Hesse is a region in the northern part of the German state of Hesse, centered around the city of Kassel and known for its forests, hills, and 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_69a88600b2f88190bc09303e68ab517e completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69abb09e714881909cef0f7e77b5b3b9 completed March 7, 2026, 4:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae5d7ffb9c8190adbe64b75bb048d4 completed March 9, 2026, 5:41 a.m.
Created at: March 4, 2026, 7:34 p.m.