Triple
T7834243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ngozumpa Glacier |
E181650
|
entity |
| Predicate | hasNearbyMountain |
P651
|
FINISHED |
| Object | Cho Oyu |
E34332
|
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: Cho Oyu | Statement: [Ngozumpa Glacier, hasNearbyMountain, Cho Oyu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cho Oyu Context triple: [Ngozumpa Glacier, hasNearbyMountain, Cho Oyu]
-
A.
Cho Oyu
chosen
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.
-
B.
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.
-
C.
Lhotse
Lhotse is the world’s fourth-highest mountain, located near Mount Everest in the Himalayas on the border between Nepal and the Tibet Autonomous Region of China.
-
D.
Kangchenjunga
Kangchenjunga is the world’s third-highest mountain, a massive peak in the eastern Himalayas on the border between Nepal and India.
-
E.
Shishapangma
Shishapangma is one of the world’s fourteen eight-thousanders, a major Himalayan peak located entirely within Tibet, China.
- 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_69ca8284a25c8190a1a20afad30da792 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb064a47648190af2ca2b336584a92 |
completed | March 30, 2026, 11:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccec40f5e88190bc0fbb4ad99d09c6 |
completed | April 1, 2026, 9:58 a.m. |
Created at: March 30, 2026, 4:45 p.m.