Triple

T4581690
Position Surface form Disambiguated ID Type / Status
Subject Aarhus E101867 entity
Predicate demonym P191 FINISHED
Object Aarhusian E101867 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: Aarhusian | Statement: [Aarhus, demonym, Aarhusian]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Aarhusian
Context triple: [Aarhus, demonym, Aarhusian]
  • A. Eastphalian
    Eastphalian is a regional variety of Low German traditionally spoken in parts of central northern Germany, particularly around the historical region of Eastphalia.
  • B. Aarhus chosen
    Aarhus is Denmark’s second-largest city, a major cultural and economic center on the Jutland peninsula known for its universities, vibrant arts scene, and historic harbor.
  • C. Angrense
    An Angrense is a resident or native of Angra do Heroísmo, a historic city on Terceira Island in Portugal’s Azores archipelago.
  • D. Gothenburger
    A Gothenburger is a resident or native of Gothenburg, Sweden’s second-largest city and a major port on the country’s west coast.
  • E. Hornbæk
    Hornbæk is a coastal town in northern Zealand, Denmark, known for its sandy beaches, holiday villas, and role as a popular seaside resort.
  • 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_69bd43d4ce208190b53158c882b222e3 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd590116e88190b8495b2a78cf3fb6 completed March 20, 2026, 2:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69bde09015c48190b4f992f3f95023cf completed March 21, 2026, 12:04 a.m.
Created at: March 20, 2026, 1:10 p.m.