Triple
T2326294
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Big Country |
E48294
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Robert Swink |
E255026
|
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: Robert Swink | Statement: [The Big Country, editedBy, Robert Swink]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Robert Swink Context triple: [The Big Country, editedBy, Robert Swink]
-
A.
Robert Swink
chosen
Robert Swink was an American film editor known for his work on numerous classic Hollywood films, including "Roman Holiday."
-
B.
Gilbert Stork
Gilbert Stork was a prominent 20th-century organic chemist renowned for pioneering methods in stereoselective synthesis and significantly advancing the field of synthetic organic chemistry.
-
C.
Hans Bonte
Hans Bonte is a Belgian politician known for serving as the mayor of Vilvoorde and as a member of the federal parliament.
-
D.
Philip Steuer
Philip Steuer is a film producer best known for his work on major studio projects, including the Disney drama "Saving Mr. Banks."
-
E.
Ron Jensen
Ron Jensen is an American politician who has served as the mayor of Grand Prairie, Texas.
- 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_69a88aa308a88190b0b86c011fda7fce |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc64b62a08190b5a415769ce42645 |
completed | March 7, 2026, 6:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae9615f000819092f9dc4700998b25 |
completed | March 9, 2026, 9:42 a.m. |
Created at: March 4, 2026, 7:50 p.m.