Triple
T18756049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary Everest Boole |
E458651
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object | George Everest |
—
|
NE NERFINISHED |
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: George Everest | Statement: [Mary Everest Boole, relative, George Everest]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: George Everest Context triple: [Mary Everest Boole, relative, George Everest]
-
A.
George Everest
chosen
George Everest was a 19th-century British surveyor and geographer who served as Surveyor General of India and lent his name to Mount Everest.
-
B.
Everest
Everest is the codename for the high-performance CPU cores used in Apple’s A16 Bionic chip.
-
C.
Everest
Everest is a snow rescue pup from the animated children's series PAW Patrol, known for her bravery, love of the snow, and role as the team's mountain rescue specialist.
-
D.
Everest
Everest is a 2015 survival drama film that chronicles the harrowing true story of a deadly Mount Everest expedition.
-
E.
Mount Everest
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d395dba0819087568404508590cb |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e579f20b808190833e29830bfed937 |
completed | April 20, 2026, 12:57 a.m. |
Created at: April 10, 2026, 11:51 a.m.