Triple
T20811627
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Schaul |
E512320
|
entity |
| Predicate | coAuthorWith |
P398
|
FINISHED |
| Object | Ziyu Wang |
—
|
NE NERFINISHED |
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: Ziyu Wang | Statement: [Tom Schaul, coAuthorWith, Ziyu Wang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ziyu Wang Context triple: [Tom Schaul, coAuthorWith, Ziyu Wang]
-
A.
Ziyu Wang
chosen
Ziyu Wang is a machine learning researcher best known for co-developing the dueling deep Q-network (Dueling DQN) architecture in deep reinforcement learning.
-
B.
Yanluo Wang
Yanluo Wang is the Chinese deity who presides over the underworld and judges the souls of the dead.
-
C.
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.
-
D.
Jin Wang
Jin Wang is the Chinese American teenage protagonist of Gene Luen Yang’s graphic novel "American Born Chinese," whose story explores identity, assimilation, and cultural conflict.
-
E.
Yanqi Zhou
Yanqi Zhou is a computer scientist and researcher known for her work in natural language processing and for helping develop the T5 text-to-text transformer model at Google.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4cd25088190b48ca9700cd24efc |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2d338ac819096d4a33de831609e |
completed | April 21, 2026, 12:20 a.m. |
Created at: April 16, 2026, 12:40 p.m.