Triple
T7573750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Province No. 1, Nepal |
E179309
|
entity |
| Predicate | hasHighestPoint |
P210
|
FINISHED |
| Object | Sagarmatha |
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: Sagarmatha | Statement: [Province No. 1, Nepal, hasHighestPoint, Sagarmatha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sagarmatha Context triple: [Province No. 1, Nepal, hasHighestPoint, Sagarmatha]
-
A.
Everest
Everest is a 2015 survival drama film that chronicles the harrowing true story of a deadly Mount Everest expedition.
-
B.
Everest
Everest is the codename for the high-performance CPU cores used in Apple’s A16 Bionic chip.
-
C.
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.
-
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.
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_69c69f316e50819081a271c85c06f918 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f948e1e08190ad807292365a0c27 |
completed | March 27, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c90098f65c8190a3130a8c1aad5e7b |
completed | March 29, 2026, 10:36 a.m. |
Created at: March 27, 2026, 3:51 p.m.