Triple
T6189945
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amir Mokri |
E138159
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Amir Mokri |
E138159
|
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: Amir Mokri | Statement: [Amir Mokri, name, Amir Mokri]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amir Mokri Context triple: [Amir Mokri, name, Amir Mokri]
-
A.
Amir Mokri
chosen
Amir Mokri is an Iranian-American cinematographer known for his dynamic, high-energy visual style on major action and blockbuster films such as "Man of Steel," "Transformers: Dark of the Moon," and "Fast & Furious."
-
B.
Karim Sanjabi
Karim Sanjabi was an influential Iranian nationalist politician, lawyer, and academic who became a leading figure of the National Front and a prominent opponent of the Pahlavi monarchy.
-
C.
Karim Khalili
Karim Khalili is an Afghan politician and former vice president who served as a prominent Hazara leader and key figure in the anti-Taliban resistance.
-
D.
Rasoul Azadani
Rasoul Azadani is a film cinematographer best known for his work on Disney’s animated feature "Tangled."
-
E.
Mehdi Mirza
Mehdi Mirza is a machine learning researcher known for his contributions to deep reinforcement learning and generative models.
- 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_69c008a8fd408190b7ec6e42934974a6 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0621abad48190acb9ec019c065ed4 |
completed | March 22, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c16f0d5a2881908564442aca29b1ec |
completed | March 23, 2026, 4:49 p.m. |
Created at: March 22, 2026, 4:19 p.m.