Triple

T14161963
Position Surface form Disambiguated ID Type / Status
Subject Drakesteijn E350969 entity
Predicate municipality P852 FINISHED
Object Baarn E111040 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: Baarn | Statement: [Drakesteijn, municipality, Baarn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Baarn
Context triple: [Drakesteijn, municipality, Baarn]
  • A. Baarn chosen
    Baarn is a town and municipality in the Dutch province of Utrecht, known for its historic royal connections and green, affluent residential character.
  • B. Stadshagen
    Stadshagen is a residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
  • C. Soest
    Soest is a Dutch town and municipality in the central Netherlands known for its green surroundings and proximity to the Utrechtse Heuvelrug.
  • D. Soest
    Soest is a historic town in North Rhine-Westphalia, Germany, known for its well-preserved medieval architecture and former significance as a Hanseatic trading center.
  • E. Borghorst
    Borghorst is a district of the German town Steinfurt in North Rhine-Westphalia, known historically for its textile industry and regional cultural heritage.
  • 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_69d8278775fc8190b0802d22ca2f495d completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de613a4a2081908fd51bf4b4d82b6c completed April 14, 2026, 3:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd193b72f48190b80ac30d32ab8349 completed May 7, 2026, 10:59 p.m.
Created at: April 10, 2026, 12:59 a.m.