Triple

T4006528
Position Surface form Disambiguated ID Type / Status
Subject Christian Christiansen E89538 entity
Predicate name P16 FINISHED
Object Christian Christiansen E89538 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: Christian Christiansen | Statement: [Christian Christiansen, name, Christian Christiansen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Christian Christiansen
Context triple: [Christian Christiansen, name, Christian Christiansen]
  • A. Christian Christiansen chosen
    Christian Christiansen was a Danish physicist and professor known for his work in optics and thermodynamics and for mentoring notable scientists, including Niels Bohr.
  • B. Christian Møller
    Christian Møller was a Danish theoretical physicist known for his contributions to quantum electrodynamics and the theory of relativity.
  • C. Christian Michelsen
    Christian Michelsen was a Norwegian statesman and prime minister best known for leading the peaceful dissolution of the union between Norway and Sweden in 1905.
  • D. Kristian Kristiansen
    Kristian Kristiansen is an adventurer known for participating in an expedition that crossed the Greenland ice cap.
  • E. Folmar Blangsted
    Folmar Blangsted was a Danish-born American film editor known for his work on major Hollywood productions in the mid-20th century.
  • 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_69aed9585e788190bec2d39deba3750f completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefa60c500819084fcba785b2bf801 completed March 9, 2026, 4:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69b55626f19c8190af4705b1cd3b201d completed March 14, 2026, 12:35 p.m.
Created at: March 9, 2026, 3:34 p.m.