Triple
T23515114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ben Whittaker |
E574337
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | About The Fit |
—
|
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: About The Fit | Statement: [Ben Whittaker, employer, About The Fit]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: About The Fit Context triple: [Ben Whittaker, employer, About The Fit]
-
A.
About The Fit
chosen
About The Fit is the fast-growing online fashion startup featured in the film "The Intern," known for its innovative e-commerce approach and youthful workplace culture.
-
B.
/fit/
/fit/ is 4chan’s fitness board, dedicated to discussions about exercise, bodybuilding, weight loss, nutrition, and general physical self-improvement.
-
C.
The Fit
The Fit is a literary work by British novelist and critic Philip Hensher, known for his sharp social observation and nuanced character portrayal.
-
D.
FIT-A
FIT-A is a subdetector module of the Fast Interaction Trigger system used in high-energy physics experiments to provide rapid detection and timing of particle collisions.
-
E.
FIT
FIT is a software testing framework designed to facilitate collaboration between developers and customers by expressing and automatically checking requirements in tabular form.
- 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_69e245bb3dcc8190ba9a2b35972b58d0 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1aa80d9048190ab735dddd301feb4 |
completed | April 29, 2026, 6:51 a.m. |
Created at: April 17, 2026, 6:08 p.m.