Triple
T2199276
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Need for Speed (2014 film) |
E50450
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Aaron Paul |
E241425
|
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: Aaron Paul | Statement: [Need for Speed (2014 film), stars, Aaron Paul]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aaron Paul Context triple: [Need for Speed (2014 film), stars, Aaron Paul]
-
A.
Aaron Paul
chosen
Aaron Paul is an American actor best known for his Emmy-winning role as Jesse Pinkman in the television series "Breaking Bad."
-
B.
Logan Marshall-Green
Logan Marshall-Green is an American actor and director known for his roles in films like "Prometheus" and "Upgrade" as well as various television series.
-
C.
Ben Foster
Ben Foster is an American actor known for his intense, often gritty performances in films such as "3:10 to Yuma," "Hell or High Water," and "The Messenger."
-
D.
Christopher Abbott
Christopher Abbott is an American actor known for his roles in independent films and television series, including his breakout performance in the HBO series "Girls."
-
E.
Matt Bomer
Matt Bomer is an American actor known for his roles in the TV series "White Collar" and films such as "Magic Mike" and "The Normal Heart."
- 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_69a88b044ab48190add007487680f009 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abbf9e99f08190892d34485c8f2f25 |
completed | March 7, 2026, 6:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae6544c000819095aa63be5b1ed4d6 |
completed | March 9, 2026, 6:14 a.m. |
Created at: March 4, 2026, 7:46 p.m.