Triple

T4433469
Position Surface form Disambiguated ID Type / Status
Subject Frankfurt Airport E95589 entity
Predicate locatedIn P40 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: [Frankfurt Airport, locatedIn, Hesse]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hesse
Context triple: [Frankfurt Airport, locatedIn, 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. Kurhessen
    Kurhessen was a historical region in central Germany that formed the core territory of the Electorate of Hesse (Hesse-Kassel) within the Holy Roman Empire and later German states.
  • D. Rübeland
    Rübeland is a village in the Harz Mountains of central Germany, known for its show caves and scenic natural surroundings.
  • E. Odenwald
    Odenwald is a low mountain range in southwestern Germany known for its forested hills, historic towns, and scenic hiking 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_69b3453ea2b48190a26f154b3b8fece5 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b35587bc048190aee8e0ed94b6e064 completed March 13, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69b6137171148190b77a6f783d5cf315 completed March 15, 2026, 2:03 a.m.
Created at: March 12, 2026, 11:31 p.m.