Triple
T18724747
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benjamin Chess |
E457870
|
entity |
| Predicate | coAuthorWith |
P398
|
FINISHED |
| Object | Amanda Askell |
—
|
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: Amanda Askell | Statement: [Benjamin Chess, coAuthorWith, Amanda Askell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amanda Askell Context triple: [Benjamin Chess, coAuthorWith, Amanda Askell]
-
A.
Amanda Askell
chosen
Amanda Askell is an AI researcher and ethicist known for her work on AI alignment and safety, including contributions at OpenAI.
-
B.
Nicole Rocklin
Nicole Rocklin is an American film producer best known for co-producing the Academy Award–winning investigative drama "Spotlight."
-
C.
Amanda Detmer
Amanda Detmer is an American actress known for her roles in early 2000s films and television series, often appearing in comedies and romantic comedies.
-
D.
Skylar Tibbits
Skylar Tibbits is an architect, designer, and computer scientist known for pioneering 4D printing and programmable materials through his leadership of MIT’s Self-Assembly Lab.
-
E.
Danielle Feinberg
Danielle Feinberg is an American cinematographer and visual effects artist best known for her lighting and camera work on numerous Pixar animated films.
- 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.