Triple

T7544508
Position Surface form Disambiguated ID Type / Status
Subject A1 motorway E178363 entity
Predicate passesNear P416 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: [A1 motorway, passesNear, Baarn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Baarn
Context triple: [A1 motorway, passesNear, 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 historic town in North Rhine-Westphalia, Germany, known for its well-preserved medieval architecture and former significance as a Hanseatic trading center.
  • D. Soest
    Soest is a Dutch town and municipality in the central Netherlands known for its green surroundings and proximity to the Utrechtse Heuvelrug.
  • E. Stadthagen
    Stadthagen is a historic town in Lower Saxony, Germany, known for its Renaissance architecture and role as an administrative and cultural center of the surrounding region.
  • 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_69c69f2be3888190a6667a27f8f195e9 completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f898069881909fa8f9c885c4565b completed March 27, 2026, 9:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69c84f21aa5c819085e8d1ecbd9b01e3 completed March 28, 2026, 9:58 p.m.
Created at: March 27, 2026, 3:48 p.m.