Triple
T3638102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vizio |
E77119
|
entity |
| Predicate | founder |
P104
|
FINISHED |
| Object | William Wang |
E379108
|
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: William Wang | Statement: [Vizio, founder, William Wang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: William Wang Context triple: [Vizio, founder, William Wang]
-
A.
William Wang
chosen
William Wang is a Taiwanese-American entrepreneur best known as the founder and longtime CEO of the consumer electronics company Vizio.
-
B.
John Cheng
John Cheng is a film producer best known for his work on the dark comedy movie "Horrible Bosses."
-
C.
Kenneth Hsu
Kenneth Hsu is a Swiss geologist and oceanographer known for his influential work on marine geology and the Messinian salinity crisis.
-
D.
Yuhuai Wu
Yuhuai Wu is an AI researcher and entrepreneur known for his work on large language models and as a member of Elon Musk’s xAI team.
-
E.
Daniel Zhang
Daniel Zhang is a Chinese business executive best known for leading Alibaba Group through a major period of global expansion and for creating the Singles’ Day shopping festival.
- 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_69ad85dd0be48190b738990cb20c4731 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc328e5e481909d26318c743bc84a |
completed | March 8, 2026, 6:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4cddcfeec8190920648ec66b2a53c |
completed | March 14, 2026, 2:54 a.m. |
Created at: March 8, 2026, 3:24 p.m.