Triple

T20903518
Position Surface form Disambiguated ID Type / Status
Subject Alzey E514731 entity
Predicate state P87 FINISHED
Object Rhineland-Palatinate NE NERFINISHED

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: Rhineland-Palatinate | Statement: [Alzey, state, Rhineland-Palatinate]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rhineland-Palatinate
Context triple: [Alzey, state, Rhineland-Palatinate]
  • A. Rhineland-Palatinate chosen
    Rhineland-Palatinate is a federal state in western Germany known for its wine-growing regions along the Rhine and Moselle rivers and its historic cities such as Mainz and Trier.
  • B. North Rhine-Westphalia
    North Rhine-Westphalia is Germany’s most populous federal state, known for its major industrial regions, cultural hubs like Cologne and Düsseldorf, and numerous universities and research institutions.
  • C. Hesseng
    Hesseng is a small village in the municipality of Sør-Varanger in Troms og Finnmark county in northern Norway.
  • D. 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.
  • E. Saarland
    Saarland is a small federal state in southwestern Germany known for its industrial history, Franco-German cultural influences, and location along the borders with France and Luxembourg.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4f8a1108190bce3d31331290ced completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6e8fe4b808190bbc1bbde7a11f283 completed April 21, 2026, 3:03 a.m.
Created at: April 16, 2026, 12:47 p.m.