Triple
T19634107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom B. Brown |
E471343
|
entity |
| Predicate | coAuthorWith |
P398
|
FINISHED |
| Object | Sam McCandlish |
—
|
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: Sam McCandlish | Statement: [Tom B. Brown, coAuthorWith, Sam McCandlish]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sam McCandlish Context triple: [Tom B. Brown, coAuthorWith, Sam McCandlish]
-
A.
Sam McCandlish
chosen
Sam McCandlish is a machine learning researcher known for his work on large-scale language models and contributions to influential AI research at OpenAI.
-
B.
Andrew Pyle
Andrew Pyle is a British philosopher known for his work in the philosophy of science, metaphysics, and the history of early modern philosophy.
-
C.
Matthew Shafer
Matthew Shafer is an American writer known for his work on the animated series "Cowboy Bebop" and related projects.
-
D.
Matthew Shafer
Matthew Shafer, better known by his stage name Uncle Kracker, is an American singer-songwriter and musician recognized for his blend of rock, country, and pop influences.
-
E.
Sam Healy
Sam Healy is a fictional prison counselor and correctional officer in the television series "Orange Is the New Black."
- 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_69d8e511f28481909f4bc3ea9191e54a |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e64104ff2881908fec49b7fba5a2e6 |
completed | April 20, 2026, 3:06 p.m. |
Created at: April 10, 2026, 1:44 p.m.