Triple
T18724613
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pranav Shyam |
E457864
|
entity |
| Predicate | coAuthorWith |
P398
|
FINISHED |
| Object | Benjamin Chess |
—
|
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: Benjamin Chess | Statement: [Pranav Shyam, coAuthorWith, Benjamin Chess]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Benjamin Chess Context triple: [Pranav Shyam, coAuthorWith, Benjamin Chess]
-
A.
Benjamin Chess
chosen
Benjamin Chess is a computer scientist and AI researcher known for co-authoring influential work in large-scale language models alongside figures such as Tom B. Brown.
-
B.
Benjamin Griffith
Benjamin Griffith was an early settler and landowner whose contributions to the area led to the Indiana town of Griffith being named in his honor.
-
C.
Arthur Guez
Arthur Guez is a machine learning researcher known for his contributions to deep reinforcement learning, including co-developing the Double DQN algorithm.
-
D.
Christopher Chessun
Christopher Chessun is an Anglican bishop who serves as a senior Church of England leader in the Diocese of Southwark.
-
E.
Arthur Schoenfeld
Arthur Schoenfeld was an American diplomat who served as the United States Ambassador to Hungary during the mid-20th century.
- 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_69d8d393ba9c8190a8b03b04ddbb0a09 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e56d72d2c4819080b0d31860976b5e |
completed | April 20, 2026, 12:04 a.m. |
Created at: April 10, 2026, 11:50 a.m.