Triple
T20174149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GMA Day |
E492044
|
entity |
| Predicate | presenter |
P83
|
FINISHED |
| Object | Sara Haines |
—
|
NE NERFINISHED |
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 Haines | Statement: [GMA Day, presenter, Sara Haines]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sara Haines Context triple: [GMA Day, presenter, Sara Haines]
-
A.
Sara Haines
chosen
Sara Haines is an American television host and journalist best known as a co-host of the daytime talk show "The View" and for her work on various ABC programs.
-
B.
Molly Gordon
Molly Gordon is an American actress and director known for her roles in films like "Booksmart" and "Good Boys" and the TV series "The Bear."
-
C.
Madeleine Lesser
Madeleine Lesser is the wife of British actor Anton Lesser, known for his work in television, film, and theatre.
-
D.
Maya Erskine
Maya Erskine is an American actress, writer, and comedian best known for co-creating and starring in the cringe-comedy series "PEN15."
-
E.
Jenny Slate
Jenny Slate is an American actress, comedian, and writer known for her distinctive voice work in animated films and series as well as roles in indie comedies and television.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da6266c6888190bc1a3ecf24814d34 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e6684a33688190b22cfc16907e76bc |
completed | April 20, 2026, 5:54 p.m. |
Created at: April 11, 2026, 11:36 p.m.