Triple

T4671229
Position Surface form Disambiguated ID Type / Status
Subject Margaret, Maid of Norway E102966 entity
Predicate birthPlace P1 FINISHED
Object Tønsberg, Norway E112118 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: Tønsberg, Norway | Statement: [Margaret, Maid of Norway, birthPlace, Tønsberg, Norway]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tønsberg, Norway
Context triple: [Margaret, Maid of Norway, birthPlace, Tønsberg, Norway]
  • A. Folkestad, Norway
    Folkestad, Norway is a small village in Norway historically notable as the birthplace of King Haakon IV.
  • B. Ballangen, Norway
    Ballangen, Norway is a small former mining municipality in Nordland county known for its scenic fjord landscape in Northern Norway.
  • C. Tønsberg chosen
    Tønsberg is a historic coastal town in southeastern Norway, often regarded as one of the country’s oldest cities and known for its Viking heritage and maritime culture.
  • D. Lysaker, Norway
    Lysaker, Norway is a suburban area in Bærum just west of Oslo, known as a residential and commercial hub and historically associated with notable figures such as explorer Fridtjof Nansen.
  • E. Hamar, Norway
    Hamar, Norway is a town in southeastern Norway known for its prominent ice sports facilities and role as a major venue for international speed skating competitions.
  • 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_69bd43d9cba4819086c1ab1c2d9d2133 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd63506090819083ff8271adc5ef75 completed March 20, 2026, 3:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69be039538048190b4075daf47355cee completed March 21, 2026, 2:33 a.m.
Created at: March 20, 2026, 1:15 p.m.