Triple
T4290481
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George Everest |
E97375
|
entity |
| Predicate | hasPartNamedAfter |
P63
|
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 Everest, hasPartNamedAfter, Mount Everest]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mount Everest Context triple: [George Everest, hasPartNamedAfter, 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.
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.
-
E.
Kangchenjunga
Kangchenjunga is the world’s third-highest mountain, a massive peak in the eastern Himalayas on the border between Nepal and India.
- 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_69b3454595848190a0e6bbb6a2bea040 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3507efb28819091a9d5b9161a5008 |
completed | March 12, 2026, 11:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5db874fb08190974a85cc139b020b |
completed | March 14, 2026, 10:04 p.m. |
Created at: March 12, 2026, 11:08 p.m.