Triple
T15876287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nima Yooshij |
E384960
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Nima Yooshij |
E384960
|
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: Nima Yooshij | Statement: [Nima Yooshij, name, Nima Yooshij]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nima Yooshij Context triple: [Nima Yooshij, name, Nima Yooshij]
-
A.
Nima Yooshij
chosen
Nima Yooshij was a pioneering Iranian poet widely regarded as the father of modern Persian poetry for revolutionizing its form and style.
-
B.
Paul Song
Paul Song is an American physician and healthcare activist known for his work in progressive politics and as the husband of journalist Lisa Ling.
-
C.
Aaron Yoo
Aaron Yoo is an American actor known for his supporting roles in films like "Disturbia," "21," and "Nick and Norah's Infinite Playlist," as well as various television appearances.
-
D.
Frank Wang
Frank Wang is a Chinese entrepreneur and engineer best known as the founder and CEO of DJI, the world’s leading consumer drone manufacturer.
-
E.
Taesung Park
Taesung Park is a computer vision researcher best known for co-authoring the CycleGAN framework for unpaired image-to-image translation.
- 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_69d86da4e86481909f1325fdc971b5ec |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e155fdc1b881909d1c82c4c66a195a |
completed | April 16, 2026, 9:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa94e9b548190bec74e6d9790d241 |
completed | May 9, 2026, 9:38 p.m. |
Created at: April 10, 2026, 4:51 a.m.