Triple
T14697964
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Master Builder |
E345212
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Julie Hagerty |
—
|
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: Julie Hagerty | Statement: [A Master Builder, castMember, Julie Hagerty]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Julie Hagerty Context triple: [A Master Builder, castMember, Julie Hagerty]
-
A.
Julie Hagerty
chosen
Julie Hagerty is an American actress and former model best known for her comedic roles in films such as "Airplane!" and "What About Bob?".
-
B.
Julie Ferguson
Julie Ferguson is the wife of the late American actor and comedian Charles Grodin.
-
C.
Julie Richardson
Julie Richardson is a film producer best known for her work on the acclaimed crime thriller "Collateral."
-
D.
Lisa Hartman
Lisa Hartman is an American actress and singer best known for her role on the television series "Knots Landing" and for her country music collaborations with her husband, Clint Black.
-
E.
Julie Kirkwood
Julie Kirkwood is a cinematographer known for her work on feature films such as the thriller "Destroyer."
- 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_69d822e4a8c08190a155df736bb7bc13 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb604f88081908a677175045496d0 |
completed | April 14, 2026, 9:47 p.m. |
Created at: April 10, 2026, 1:28 a.m.