Triple

T8857477
Position Surface form Disambiguated ID Type / Status
Subject Marie Ahnighito Peary E210793 entity
Predicate residence P75 FINISHED
Object Greenland E15389 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: Greenland | Statement: [Marie Ahnighito Peary, residence, Greenland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Greenland
Context triple: [Marie Ahnighito Peary, residence, Greenland]
  • A. Greenland chosen
    Greenland is the world’s largest island, an autonomous territory within the Kingdom of Denmark, known for its vast Arctic landscapes and extensive ice sheet.
  • B. Greenland
    Greenland is a 2020 American disaster thriller film starring Gerard Butler, centered on a family's struggle to survive a catastrophic comet event.
  • C. Groenlandia
    Groenlandia is a film and television production company known for working on major international projects such as the series "Game of Thrones."
  • D. Grenland
    Grenland is a culturally and historically significant region in southeastern Norway, centered around the industrial towns near the coast and traditionally associated with the county of Telemark.
  • E. Grønland
    Grønland is a central Oslo neighborhood known for its multicultural character, vibrant street life, and diverse shops and eateries.
  • 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_69ca838bbddc8190ab546d737e5d350f completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc60e3b62c8190bf779e7e1db767f6 completed April 1, 2026, 12:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfab8911c8819083f5caa318071720 completed April 3, 2026, 11:59 a.m.
Created at: March 30, 2026, 6:50 p.m.