Triple

T13620640
Position Surface form Disambiguated ID Type / Status
Subject Elizabeth of Denmark E325440 entity
Predicate birthPlace P1 FINISHED
Object Nyborg E233013 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: Nyborg | Statement: [Elizabeth of Denmark, birthPlace, Nyborg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nyborg
Context triple: [Elizabeth of Denmark, birthPlace, Nyborg]
  • A. Nyborg chosen
    Nyborg is a historic coastal town and former royal seat in central Denmark, located on the island of Funen.
  • B. Vordingborg
    Vordingborg is a historic coastal town in southern Denmark known for the ruins of Vordingborg Castle and its prominent Goose Tower.
  • C. Svendborg
    Svendborg is a historic coastal town and seaport in southern Denmark known for its maritime heritage and location on the island of Funen.
  • D. Sønderborg
    Sønderborg is a coastal town in southern Denmark known for its historic castle, waterfront setting on the island of Als, and role as a regional cultural and educational center.
  • E. Faaborg
    Faaborg is a historic coastal town on the island of Funen in southern Denmark, known for its well-preserved old town, harbor, and cultural attractions.
  • 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_69d8076aae28819092cf636190ee5529 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbb0b0c9008190836242da2d6a8cbe completed April 12, 2026, 2:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7a83aeeb48190b92b00366791ab15 completed May 3, 2026, 7:55 p.m.
Created at: April 9, 2026, 9:50 p.m.