Triple
T9740976
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Butterfly Effect |
E236183
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object | Eric Bress |
E818792
|
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: Eric Bress | Statement: [The Butterfly Effect, writer, Eric Bress]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eric Bress Context triple: [The Butterfly Effect, writer, Eric Bress]
-
A.
Eric Bress
chosen
Eric Bress is an American screenwriter and film director best known for co-writing and co-directing the psychological thriller film "The Butterfly Effect."
-
B.
Alex Gansa
Alex Gansa is an American television writer and producer best known for co-creating and showrunning the acclaimed political thriller series "Homeland."
-
C.
Matthew Heineman
Matthew Heineman is an American filmmaker known for his immersive, character-driven documentaries and narrative features that often explore conflict zones and social issues.
-
D.
Stephen Gaghan
Stephen Gaghan is an American screenwriter and director best known for his politically charged, multi-narrative films such as "Traffic" and "Syriana."
-
E.
Jeremy Leven
Jeremy Leven is an American screenwriter, director, and novelist known for adapting romantic and character-driven stories for film, including the hit movie "The Notebook."
- 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_69ca84d3e24481908a476e2231123cf9 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9f2af3e48190b83a442cd0e84062 |
completed | April 1, 2026, 10:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1bccf62e08190ae799ee557ad1f1b |
completed | April 5, 2026, 1:37 a.m. |
Created at: March 30, 2026, 8:23 p.m.