Triple

T5207902
Position Surface form Disambiguated ID Type / Status
Subject George Leigh-Mallory E117556 entity
Predicate placeOfDeath P21 FINISHED
Object Mount Everest E11056 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: Mount Everest | Statement: [George Leigh-Mallory, placeOfDeath, Mount Everest]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mount Everest
Context triple: [George Leigh-Mallory, placeOfDeath, Mount Everest]
  • A. Mount Everest chosen
    Mount Everest is the world's highest mountain above sea level, located in the Himalayas on the border between Nepal and the Tibet Autonomous Region of China.
  • B. Everest
    Everest is the codename for the high-performance CPU cores used in Apple’s A16 Bionic chip.
  • C. Cho Oyu
    Cho Oyu is the world’s sixth-highest mountain, an 8,188-meter peak in the Mahalangur Himal section of the Himalayas near the Nepal–China border.
  • D. Everes
    Everes is a figure in Greek mythology known primarily as the father of the blind prophet Tiresias.
  • E. Makalu
    Makalu is the fifth-highest mountain in the world, a prominent 8,485-meter peak on the border between Nepal and China known for its steep faces and challenging climbing routes.
  • 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_69bd4463dd3c81909966123f20b79d57 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd7a6d70d081908c74e86b3bca9ba2 completed March 20, 2026, 4:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69bef7ffab8c81908e17e085727304b6 completed March 21, 2026, 7:56 p.m.
Created at: March 20, 2026, 1:47 p.m.