Triple
T17399761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Love & War |
E423054
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object | Michael Nouri |
—
|
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: Michael Nouri | Statement: [Love & War, hasCastMember, Michael Nouri]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Nouri Context triple: [Love & War, hasCastMember, Michael Nouri]
-
A.
Michael Nouri
chosen
Michael Nouri is an American actor best known for his roles in films like "Flashdance" and numerous television series, including "NCIS" and "Damages."
-
B.
Justin Khoury
Justin Khoury is a theoretical physicist known for his work in cosmology and modified gravity, including contributions to models of dark energy and dark matter.
-
C.
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.
-
D.
Chris Hajian
Chris Hajian is an American film and television composer known for his work on projects such as the series "StartUp" and numerous independent films.
-
E.
Younes Nazarian
Younes Nazarian was an Iranian-American businessman, philanthropist, and major benefactor of educational and cultural institutions in the United States and Israel.
- 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_69d889d710288190bf0f4762801fefae |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43ac0596481908c400916d5c1b971 |
completed | April 19, 2026, 2:15 a.m. |
Created at: April 10, 2026, 5:45 a.m.