Triple
T247210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Himalayas |
E5063
|
entity |
| Predicate | highestPoint |
P210
|
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: [Himalayas, highestPoint, Mount Everest]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mount Everest Context triple: [Himalayas, highestPoint, 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.
K2
K2 is the world’s second-highest mountain, a notoriously difficult and dangerous peak in the Karakoram range of the Himalayas.
-
C.
Mount Elbrus
Mount Elbrus is a dormant stratovolcano in the Caucasus Mountains of Russia and the highest peak on the European continent.
-
D.
Mount Kilimanjaro
Mount Kilimanjaro is a massive dormant stratovolcano in northeastern Tanzania and the tallest mountain in Africa, famed for its snow-capped summit rising above the surrounding savanna.
-
E.
Aconcagua
Aconcagua is the highest mountain in the Americas and the tallest peak outside of Asia, located in the Andes of western Argentina.
- 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_69a257c4bf688190a46ebbf411ab7473 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25d13b8088190a3f48f0388d57496 |
completed | Feb. 28, 2026, 3:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a36cf64798819096218d320b00a3a9 |
completed | Feb. 28, 2026, 10:32 p.m. |
Created at: Feb. 28, 2026, 2:54 a.m.