Triple
T5228725
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roots |
E118055
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | James Lee |
E235472
|
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: James Lee | Statement: [Roots, screenwriter, James Lee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: James Lee Context triple: [Roots, screenwriter, James Lee]
-
A.
James Lee
chosen
James Lee is a screenwriter known for his work on the film "Counterpoint."
-
B.
Mark Pellington
Mark Pellington is an American film and music video director known for his visually striking, emotionally intense work on projects such as U2’s “One” and the film “Arlington Road.”
-
C.
Lyndon Simmonds
Lyndon Simmonds is a former professional footballer best known for playing as a forward in the English leagues during the late 20th century.
-
D.
Brian David Willis
Brian David Willis is a musician best known as a member of the American rock band Quarterflash.
-
E.
Michael Ealy
Michael Ealy is an American actor known for his roles in films like "Barbershop," "Think Like a Man," and "2 Fast 2 Furious," as well as various television series.
- 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_69bd4466fb8c819083b806a79414d7e4 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7adee36881909b034b8735db9d67 |
completed | March 20, 2026, 4:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69befe604a848190a3f6cc90185b3ca2 |
completed | March 21, 2026, 8:24 p.m. |
Created at: March 20, 2026, 1:48 p.m.