Triple
T8874065
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sidney Luft |
E211224
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Sidney Luft |
E211224
|
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: Sidney Luft | Statement: [Sidney Luft, name, Sidney Luft]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sidney Luft Context triple: [Sidney Luft, name, Sidney Luft]
-
A.
Sidney Luft
chosen
Sidney Luft was an American show business figure and film producer best known as the husband and manager of Judy Garland, helping to revive her career in the 1950s.
-
B.
Sidney Lanfield
Sidney Lanfield was an American film and television director best known for his work on Hollywood comedies and genre films from the 1930s through the 1950s.
-
C.
Sidney Lee
Sidney Lee was a British biographer and literary scholar best known for his extensive work on the Dictionary of National Biography and his influential studies of William Shakespeare.
-
D.
John L. Lumley
John L. Lumley was a prominent American fluid dynamicist known for his pioneering contributions to the understanding and modeling of turbulence.
-
E.
Sidney Colbert
Sidney Colbert is an individual notable enough to be recognized as a bearer of the Colbert surname, though specific widely known biographical details about them are not well documented.
- 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_69ca838e78748190934d82db3104f855 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc614451d081908804430a72d00edf |
completed | April 1, 2026, 12:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfab9e87cc8190ae3c8c683aa0921e |
completed | April 3, 2026, 11:59 a.m. |
Created at: March 30, 2026, 6:52 p.m.