Triple
T13676007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moira O’Hara |
E327879
|
entity |
| Predicate | createdBy |
P806
|
FINISHED |
| Object | Brad Falchuk |
E303079
|
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: Brad Falchuk | Statement: [Moira O’Hara, createdBy, Brad Falchuk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brad Falchuk Context triple: [Moira O’Hara, createdBy, Brad Falchuk]
-
A.
Brad Falchuk
chosen
Brad Falchuk is an American television writer, director, and producer best known for co-creating series such as Glee, American Horror Story, and Scream Queens.
-
B.
David Fenkel
David Fenkel is an American film executive and producer best known as a co-founder of the independent entertainment company A24.
-
C.
Josh Kesselman
Josh Kesselman is a film and television producer best known for his work as an executive producer on projects such as the series "The Great."
-
D.
Aaron Korsh
Aaron Korsh is an American television writer and producer best known for creating the legal drama series "Suits."
-
E.
Steven Haft
Steven Haft is an American film producer known for his work on movies such as the cult Halloween comedy "Hocus Pocus."
- 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_69d8076f1fa8819094664a59b55010df |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc65c04988190b675e6fb7241e53c |
completed | April 12, 2026, 4:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b063661481908da569084c20f37c |
completed | May 3, 2026, 8:30 p.m. |
Created at: April 9, 2026, 9:53 p.m.