Triple

T16805155
Position Surface form Disambiguated ID Type / Status
Subject Ubbergen E408461 entity
Predicate containsSettlement P847 FINISHED
Object Persingen E408456 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: Persingen | Statement: [Ubbergen, containsSettlement, Persingen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Persingen
Context triple: [Ubbergen, containsSettlement, Persingen]
  • A. Persingen chosen
    Persingen is a small village in the Dutch province of Gelderland, often noted as one of the smallest villages in the Netherlands.
  • B. Petershausen
    Petershausen is a Bavarian municipality in southern Germany, located north of Munich and known for its rural character and good rail connections to the city.
  • C. Schöppingen
    Schöppingen is a small municipality in North Rhine-Westphalia, Germany, known for its rural character and location near the Dutch border.
  • D. Birkenwerder
    Birkenwerder is a small municipality in the German state of Brandenburg, located just north of Berlin and known for its residential character and surrounding forests.
  • E. Schönholz
    Schönholz is a locality in Berlin, Germany, served by the city’s S-Bahn rapid transit network.
  • 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_69d88393905081908d00a86b99996ac8 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3b2cb68508190a05749bad68f7b43 completed April 18, 2026, 4:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00b28d3a808190bc94a4f09a10da7e completed May 10, 2026, 4:30 p.m.
Created at: April 10, 2026, 5:22 a.m.