Triple

T8202086
Position Surface form Disambiguated ID Type / Status
Subject Nobitz E191601 entity
Predicate partOf P40 FINISHED
Object Altenburger Land E693397 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: Altenburger Land | Statement: [Nobitz, partOf, Altenburger Land]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Altenburger Land
Context triple: [Nobitz, partOf, Altenburger Land]
  • A. Altenburger Land chosen
    Altenburger Land is a rural district in the eastern German state of Thuringia, known for its historic town of Altenburg and traditional card game Skat.
  • B. Ansbach region
    The Ansbach region is an area in the German state of Bavaria, historically part of Franconia and known for its distinct East Franconian dialect and cultural heritage.
  • C. Harz district
    Harz district is an administrative district in central Germany known for encompassing much of the Harz mountain range, including historic towns and natural landscapes.
  • D. Saalekreis
    Saalekreis is a rural district in the German state of Saxony-Anhalt, located around the city of Halle (Saale).
  • E. Salzlandkreis
    Salzlandkreis is a rural district in the German state of Saxony-Anhalt known for its agricultural landscape and small towns along rivers such as the Bode and Saale.
  • 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_69ca82c7f3e08190857bf1fc63b2a10c completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb5df84b108190b4407a72a3500af9 completed March 31, 2026, 5:39 a.m.
NED1 Entity disambiguation (via context triple) batch_69cd94c7db688190a755d0143c71c2b2 completed April 1, 2026, 9:57 p.m.
Created at: March 30, 2026, 5:43 p.m.