Triple
T1678034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | About Time |
E36276
|
entity |
| Predicate | cinematography |
P1953
|
FINISHED |
| Object | John Guleserian |
E124336
|
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: John Guleserian | Statement: [About Time, cinematography, John Guleserian]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Guleserian Context triple: [About Time, cinematography, John Guleserian]
-
A.
John Guleserian
chosen
John Guleserian is an American cinematographer known for his work on independent films and romantic dramas, including the acclaimed feature "Like Crazy."
-
B.
Andrew Miano
Andrew Miano is an American film producer known for his work on independent and critically acclaimed movies, often collaborating with director Tom Ford and others.
-
C.
Joe Mantello
Joe Mantello is an acclaimed American actor and director, particularly renowned for his work on Broadway in both plays and musicals.
-
D.
Michael Vartan
Michael Vartan is a French-American actor best known for his role as CIA agent Michael Vaughn on the television series "Alias."
-
E.
Joel McNeely
Joel McNeely is an American composer and conductor best known for his work on film and television scores, including numerous projects for Disney and other major studios.
- 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_69a886139ed081909af0940aa9313512 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa625f7e1081909c3c4fe76625783a |
completed | March 6, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad71ba4db08190a532fb334fd0cd23 |
completed | March 8, 2026, 12:55 p.m. |
Created at: March 4, 2026, 7:29 p.m.