Triple
T13933119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Keynote Speaker |
E335040
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | "Mt. 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: "Mt. Everest" | Statement: [The Keynote Speaker, hasPart, "Mt. Everest"]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: "Mt. Everest" Context triple: [The Keynote Speaker, hasPart, "Mt. Everest"]
-
A.
Everest
Everest is the codename for the high-performance CPU cores used in Apple’s A16 Bionic chip.
-
B.
Everest
Everest is a 2015 survival drama film that chronicles the harrowing true story of a deadly Mount Everest expedition.
-
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.
Everest massif
The Everest massif is the high Himalayan mountain group centered around Mount Everest, comprising several of the world’s tallest peaks clustered along the Nepal–Tibet border.
-
E.
Katlang
Katlang is a town and administrative unit in Pakistan’s Khyber Pakhtunkhwa province, situated within the Mardan region and known for its agricultural surroundings and local markets.
- 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_69d81c5f739081908bc05b2461f54828 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2cf28df081908d897d7b9ec7939d |
completed | April 14, 2026, 12:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7ce865ab4819088221189344b3801 |
completed | May 3, 2026, 10:39 p.m. |
Created at: April 9, 2026, 10:17 p.m.