Triple
T3633374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Kite Runner (film) |
E77009
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object | Matt Chesse |
E224800
|
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: Matt Chesse | Statement: [The Kite Runner (film), editor, Matt Chesse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matt Chesse Context triple: [The Kite Runner (film), editor, Matt Chesse]
-
A.
Matt Chesse
chosen
Matt Chesse is an American film editor known for his work on numerous feature films, including the thriller "Money Monster."
-
B.
Matthew Mottola
Matthew Mottola is the son of music executive Tommy Mottola.
-
C.
Matt Hauser
Matt Hauser is a composer and musician known for creating the score for the documentary film "Enron: The Smartest Guys in the Room."
-
D.
Brant Daugherty
Brant Daugherty is an American actor known for his roles in television series like "Pretty Little Liars" and films including the "Fifty Shades" franchise.
-
E.
Matt Graver
Matt Graver is a seasoned and morally ambiguous CIA operative who orchestrates covert operations against Mexican drug cartels in the film "Sicario."
- 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_69ad85dd0be48190b738990cb20c4731 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc30457608190840fb5b33f9965c4 |
completed | March 8, 2026, 6:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b503df6c708190b8adf4afcadde20c |
completed | March 14, 2026, 6:44 a.m. |
Created at: March 8, 2026, 3:23 p.m.