Triple
T19532719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ATL (2006 film) |
E488693
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Lauren London |
—
|
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: Lauren London | Statement: [ATL (2006 film), stars, Lauren London]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lauren London Context triple: [ATL (2006 film), stars, Lauren London]
-
A.
Lauren London
chosen
Lauren London is an American actress and model known for her roles in film and television, including notable performances in projects like "ATL" and "The Game."
-
B.
Lauren Lloyd
Lauren Lloyd is a film producer best known for her work on the 1990 coming-of-age drama "Mermaids."
-
C.
Lauren Boyle
Lauren Boyle is a New Zealand freestyle swimmer and multiple World Championship medallist known for her success in middle- and long-distance events.
-
D.
Yasmin Paige
Yasmin Paige is a British actress best known for her roles in television and film, including the coming-of-age movie "Submarine" and various UK drama series.
-
E.
Jamie Cudmore
Jamie Cudmore is a former Canadian rugby union lock known for his long professional career in France and his physical, hard-hitting style of play.
- 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_69d8e8db5b6c8190984b61f91981f575 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6364091f4819088b27d0ffdf6010d |
completed | April 20, 2026, 2:20 p.m. |
Created at: April 10, 2026, 1:41 p.m.