Triple

T13568398
Position Surface form Disambiguated ID Type / Status
Subject Mini Countryman E324096 entity
Predicate assemblyLocation P40 FINISHED
Object Leipzig, Germany E38199 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: Leipzig, Germany | Statement: [Mini Countryman, assemblyLocation, Leipzig, Germany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Leipzig, Germany
Context triple: [Mini Countryman, assemblyLocation, Leipzig, Germany]
  • A. Leipzig chosen
    Leipzig is a major city in eastern Germany known for its rich cultural heritage, vibrant music and arts scene, and important role in trade and commerce.
  • B. Döberitz, Germany
    Döberitz, Germany is a locality historically known for its military airfield and role in early German aviation testing and development.
  • C. Brühl, Germany
    Brühl, Germany is a town in North Rhine-Westphalia known for its UNESCO-listed Augustusburg and Falkenlust palaces and its proximity to Cologne.
  • D. Linden, Germany
    Linden, Germany is a small town in the state of Hesse known for its historical roots and traditional German character.
  • E. Johnsburg, Germany
    Johnsburg, Germany is a German town that served as the namesake and ancestral origin for many of the settlers of Johnsburg, Illinois.
  • 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_69d8076830b48190910a902bae5888e2 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbb00e0188819094fde44f85adb69c completed April 12, 2026, 2:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7942a29b88190acefc8b3b91d849f completed May 3, 2026, 6:30 p.m.
Created at: April 9, 2026, 9:48 p.m.