Triple
T11338586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Green |
E268535
|
entity |
| Predicate | cofounderOf |
P4562
|
FINISHED |
| Object | Crash Course |
E290282
|
NE FINISHED |
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: Crash Course | Statement: [John Green, cofounderOf, Crash Course]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Crash Course Context triple: [John Green, cofounderOf, Crash Course]
-
A.
Crash Course
chosen
Crash Course is an educational YouTube series that offers fast-paced, engaging video lessons on a wide range of academic subjects.
-
B.
Watch n' Learn
"Watch n' Learn" is a playful, reggae-infused R&B song by Rihanna from her album *Talk That Talk*.
-
C.
Crash
Crash is a controversial 1973 novel by J. G. Ballard that explores the eroticization of car crashes and the dark intersections of technology, violence, and desire.
-
D.
Crash
Crash is a 2004 ensemble drama film exploring racial and social tensions in Los Angeles through intersecting storylines.
-
E.
Crash
Crash is a hyperactive opossum character from the Ice Age animated film series, known for his comic antics alongside his twin brother Eddie.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6aacb1f0881908c84a349fd1be047 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7ea008b5081908e6c6c6fc29ef936 |
completed | April 9, 2026, 6:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5433d3e848190ad4f51c23d5a8bb2 |
completed | April 19, 2026, 9:03 p.m. |
Created at: April 8, 2026, 9:33 p.m.