Triple

T11194702
Position Surface form Disambiguated ID Type / Status
Subject Gießen E264892 entity
Predicate locatedIn P40 FINISHED
Object state of Hesse E157843 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: state of Hesse | Statement: [Gießen, locatedIn, state of Hesse]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: state of Hesse
Context triple: [Gießen, locatedIn, state of Hesse]
  • A. state of Hesse chosen
    The state of Hesse is a federal state in central Germany known for its financial hub Frankfurt am Main, extensive forests, and significant cultural and economic influence.
  • B. Province of Hesse-Nassau
    The Province of Hesse-Nassau was a Prussian administrative province in western Germany formed in the 19th century that included territories such as Hesse-Kassel and parts of Nassau.
  • C. Hamburg state
    Hamburg state is a federal state of Germany that consists primarily of the city of Hamburg, a major northern European port and cultural center.
  • D. Hesse-Kassel
    Hesse-Kassel was a German principality known for supplying large numbers of Hessian mercenary troops to fight alongside the British during the American Revolutionary War.
  • E. Baden-Württemberg
    Baden-Württemberg is a federal state in southwest Germany known for its strong economy, automotive industry, and cities like Stuttgart, Heidelberg, and Freiburg.
  • 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_69d6aa9eb9248190b20211772621b4bc completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e8bf14e481908563b15790af4d20 completed April 9, 2026, 5:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69e483f8ecf4819086f0bab3ca9ddcb4 completed April 19, 2026, 7:27 a.m.
Created at: April 8, 2026, 9:29 p.m.