Triple
T23120892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Say Anything... |
E576888
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Richard Marks |
—
|
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: Richard Marks | Statement: [Say Anything..., editedBy, Richard Marks]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Richard Marks Context triple: [Say Anything..., editedBy, Richard Marks]
-
A.
Richard Marks
chosen
Richard Marks was an American film editor known for his work on numerous acclaimed movies, including the culinary drama "Julie & Julia."
-
B.
John Marks
John Marks is an American author and journalist known for his investigative and political writing, including collaborations with fellow reporter Joseph Medill Patterson Albright.
-
C.
Matthew Marks
Matthew Marks is an influential American art dealer and gallerist known for representing prominent contemporary artists through his eponymous gallery.
-
D.
Gary Marks
Gary Marks is a screenwriter best known for his work on the music-centered drama film "The Music Never Stopped."
-
E.
Bill Marks
Bill Marks is the troubled yet determined U.S. federal air marshal portrayed by Liam Neeson in the action-thriller film "Non-Stop."
- 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_69e245f6c2e881909a228fdcfeb7c7d3 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18e4f9be881908307838bfb5edc0c |
completed | April 29, 2026, 4:51 a.m. |
Created at: April 17, 2026, 3:59 p.m.