Triple
T8822399
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 黃仁勳 |
E209936
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | 黃仁勳 |
E209936
|
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: 黃仁勳 | Statement: [黃仁勳, name, 黃仁勳]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 黃仁勳 Context triple: [黃仁勳, name, 黃仁勳]
-
A.
黃仁勳
chosen
黃仁勳（Jensen Huang）是一位台灣裔美國企業家與工程師，最知名為繪圖晶片與人工智慧晶片巨頭輝達（NVIDIA）的共同創辦人兼執行長。
-
B.
Fei-Fei Li
Fei-Fei Li is a prominent computer scientist and AI researcher known for her pioneering work in computer vision and as a leading figure in ethical and human-centered artificial intelligence.
-
C.
Diane Greene
Diane Greene is a prominent American technology executive and entrepreneur best known as a co-founder and former CEO of VMware and a former CEO of Google Cloud.
-
D.
Ginni Rometty
Ginni Rometty is an American business executive best known for serving as the first female CEO of IBM, where she led the company’s strategic shift toward cloud computing and artificial intelligence.
-
E.
Lisa Su
Lisa Su is a Taiwanese-American electrical engineer and business executive best known for leading AMD’s turnaround and growth as its chief executive.
- 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_69ca8364e13081909c85fe80f44fe86f |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc602ed87c8190ab772e5cbb2da68d |
completed | April 1, 2026, midnight |
| NED1 | Entity disambiguation (via context triple) | batch_69cf6fd3b3348190a63bfd29860cc95f |
completed | April 3, 2026, 7:44 a.m. |
Created at: March 30, 2026, 6:46 p.m.