Triple

T2378319
Position Surface form Disambiguated ID Type / Status
Subject İstanbul İmam Hatip High School E46250 entity
Predicate hasRegion P285 FINISHED
Object Eurasia E9404 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: Eurasia | Statement: [İstanbul İmam Hatip High School, hasRegion, Eurasia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eurasia
Context triple: [İstanbul İmam Hatip High School, hasRegion, Eurasia]
  • A. Eurasia chosen
    Eurasia is the vast combined continental landmass of Europe and Asia, forming the largest continuous land area on Earth.
  • B. Afro-Eurasia
    Afro-Eurasia is the vast continuous landmass comprising the continents of Africa, Europe, and Asia, forming the largest connected continental area on Earth.
  • C. Europa
    Europa is a figure in Greek mythology, a Phoenician princess famously abducted by Zeus and later the eponymous queen of Crete.
  • D. Europa
    Europa is one of Jupiter’s large icy moons, notable for its smooth frozen surface and the subsurface ocean that makes it a prime candidate in the search for extraterrestrial life.
  • E. North Asia
    North Asia is the vast, sparsely populated northern part of the Asian continent, dominated by Siberia and characterized by its cold climate and extensive forests and tundra.
  • 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_69a88a1554a48190a0180682bcf099be completed March 4, 2026, 7:37 p.m.
NER Named-entity recognition batch_69abc7974aa481908799ef2f854d1c9d completed March 7, 2026, 6:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69aea8af12bc8190a6667beb729406a3 completed March 9, 2026, 11:02 a.m.
Created at: March 4, 2026, 7:57 p.m.