Triple
T2546508
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carefree |
E57914
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Erwin Gelsey |
E224400
|
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: Erwin Gelsey | Statement: [Carefree, screenwriter, Erwin Gelsey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Erwin Gelsey Context triple: [Carefree, screenwriter, Erwin Gelsey]
-
A.
Erwin Gelsey
chosen
Erwin Gelsey was an American screenwriter best known for his work on classic Hollywood films, including contributing the story for the Fred Astaire and Ginger Rogers musical "Swing Time."
-
B.
Jean Berko Gleason
Jean Berko Gleason is an American psycholinguist best known for her pioneering "wug test," which demonstrated how children acquire morphological rules in language.
-
C.
Edmund Burns
Edmund Burns was an American actor active during the silent and early sound film eras, appearing in numerous features throughout the 1910s and 1920s.
-
D.
Douglas Shulman
Douglas Shulman is an American public official who served as the head of the U.S. Internal Revenue Service (IRS) during the late 2000s and early 2010s.
-
E.
Joan Weill
Joan Weill is an American philanthropist known for her major contributions to education, healthcare, and the arts, including significant support for institutions like Weill Cornell Medical College.
- 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_69ab4a5212d88190b989ce129f2ad87f |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd2e5152c8190b31a5e732d0dde44 |
completed | March 7, 2026, 7:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af5d0a884c81909d7f537a79ccb435 |
completed | March 9, 2026, 11:51 p.m. |
Created at: March 6, 2026, 9:47 p.m.