Triple
T3239466
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beverly Hills Cop |
E67932
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Billy Weber |
E285441
|
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: Billy Weber | Statement: [Beverly Hills Cop, editedBy, Billy Weber]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Billy Weber Context triple: [Beverly Hills Cop, editedBy, Billy Weber]
-
A.
Billy Weber
chosen
Billy Weber is an American film editor known for his long-time collaboration with director Terrence Malick on critically acclaimed films.
-
B.
Mike Krieger
Mike Krieger is a Brazilian-American entrepreneur and software engineer best known as the co-founder and former CTO of the photo-sharing social media platform Instagram.
-
C.
Rick DeWitt
Rick DeWitt is a philosopher known for his work in logic and the philosophy of science, including authorship of accessible textbooks in these areas.
-
D.
Craig Bierko
Craig Bierko is an American actor known for his work in film, television, and theater, often playing charismatic or villainous roles.
-
E.
Brian VanDeMark
Brian VanDeMark is an American historian and author known for his work on U.S. foreign policy and the Vietnam War, including coauthoring influential studies of that conflict.
- 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_69ad858d27348190abb61c280b4c86a9 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaef4c0bc819095e4f84296fe7cb6 |
completed | March 8, 2026, 5:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2774f93448190b8493b457636ae48 |
completed | March 12, 2026, 8:20 a.m. |
Created at: March 8, 2026, 3:08 p.m.