Triple

T8681050
Position Surface form Disambiguated ID Type / Status
Subject Count of Nassau-Beilstein E206037 entity
Predicate hasHistoricalRegion P915 FINISHED
Object Rhineland E60266 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: Rhineland | Statement: [Count of Nassau-Beilstein, hasHistoricalRegion, Rhineland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rhineland
Context triple: [Count of Nassau-Beilstein, hasHistoricalRegion, Rhineland]
  • A. Rhineland chosen
    The Rhineland is a historically significant region in western Germany along the Rhine River, long contested as a strategic and economic heartland in European conflicts.
  • B. Rijnland
    Rijnland is a historical region in the western Netherlands, centered around the lower Rhine delta and known for its extensive water management and polder landscapes.
  • C. Rhine-Weser region
    The Rhine-Weser region is a historical area in western Germany associated with the early homeland and formation of the Frankish people.
  • D. Westphalia
    Westphalia is a historical region in northwestern Germany known for being the site of the 1648 treaties that ended the Thirty Years' War and reshaped the political order of Europe.
  • E. Siegerland
    Siegerland is a hilly, forested region in western Germany known for its historic iron ore mining and metalworking industry.
  • 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_69ca835379688190aa06b9d98e684d58 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc4ae6d19c8190be003f7901c0468d completed March 31, 2026, 10:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69cfab3422008190a42e579a494fa841 completed April 3, 2026, 11:57 a.m.
Created at: March 30, 2026, 6:32 p.m.