Triple
T13541623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sam Stone |
E323398
|
entity |
| Predicate | createdBy |
P806
|
FINISHED |
| Object | Dale Launer |
E307577
|
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: Dale Launer | Statement: [Sam Stone, createdBy, Dale Launer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dale Launer Context triple: [Sam Stone, createdBy, Dale Launer]
-
A.
Dale Launer
chosen
Dale Launer is an American screenwriter and film producer best known for writing hit comedies such as "Dirty Rotten Scoundrels" and "My Cousin Vinny."
-
B.
Dale Beldin
Dale Beldin is a film editor best known for his work on the action-comedy movie "Beverly Hills Cop III."
-
C.
Dennis Egan
Dennis Egan is an American politician from Alaska who served as mayor of Juneau and later as a member of the Alaska State Senate.
-
D.
Dale Eunson
Dale Eunson was an American writer and magazine editor best known for his short stories and screenwriting work in mid-20th-century Hollywood.
-
E.
Duane Schuler
Duane Schuler is an American theatrical lighting designer known for his work in opera, including major productions at leading opera houses.
- 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_69d8076776248190bdf0d4fa1f85a5fc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbafd8ba10819098faadcc6adf251e |
completed | April 12, 2026, 2:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f77f84935c8190b9e41f44140066e5 |
completed | May 3, 2026, 5:01 p.m. |
Created at: April 9, 2026, 9:45 p.m.