Triple
T4035173
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady Bird |
E83810
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Nick Houy |
E161011
|
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: Nick Houy | Statement: [Lady Bird, editedBy, Nick Houy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nick Houy Context triple: [Lady Bird, editedBy, Nick Houy]
-
A.
Nick Houy
chosen
Nick Houy is a film editor known for his work on major feature films, including the 2023 movie "Barbie."
-
B.
Ken Hutchison
Ken Hutchison was a Scottish actor known for his intense character roles in film and television during the 1970s and 1980s.
-
C.
Jeff Gourson
Jeff Gourson is a film editor known for his work on movies such as the comedy "White Chicks."
-
D.
Ken Howery
Ken Howery is an American entrepreneur, venture capitalist, and co-founder of PayPal who later served as a partner at Founders Fund and as U.S. Ambassador to Sweden.
-
E.
Michael Potts
Michael Potts is an American actor known for his work in film, television, and theater, including notable roles in projects like "The Wire," "True Detective," and various Broadway productions.
- 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_69aed92f7cf0819098e0539bdcc3767f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb11d92481909aaebbc250ff45b9 |
completed | March 9, 2026, 4:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b556415ebc8190a528c7e22dbf70df |
completed | March 14, 2026, 12:36 p.m. |
Created at: March 9, 2026, 3:36 p.m.