Triple
T16249467
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juno and the Paycock |
E394460
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Sara Allgood |
E346154
|
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: Sara Allgood | Statement: [Juno and the Paycock, stars, Sara Allgood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sara Allgood Context triple: [Juno and the Paycock, stars, Sara Allgood]
-
A.
Sara Allgood
chosen
Sara Allgood was an Irish stage and film actress known for her character roles in early 20th-century theatre and classic Hollywood cinema.
-
B.
Sara Henry
Sara Henry is known as the wife of American voice actor and comedian Mike Henry, recognized for his work on shows like Family Guy.
-
C.
Sara Howard
Sara Howard is a pioneering female secretary-turned-detective in 1890s New York City, featured prominently in Caleb Carr’s historical crime novel series "The Alienist."
-
D.
Sara Ellis
Sara Ellis is a savvy insurance investigator and Neal Caffrey’s complex love interest in the television series "White Collar."
-
E.
Sara Haden
Sara Haden was an American character actress best known for her supporting roles in classic Hollywood films of the 1930s and 1940s, including several entries in the Andy Hardy series.
- 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_69d87f2171208190951025e526947816 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e24594f23c8190bd59fcb2585cb5e3 |
completed | April 17, 2026, 2:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00dbf46cf881909f6c16f7a3d9a535 |
completed | May 10, 2026, 7:26 p.m. |
Created at: April 10, 2026, 5:04 a.m.