Triple
T9112334
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Riz Ahmed |
E218632
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Riz Ahmed |
E218632
|
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: Riz Ahmed | Statement: [Riz Ahmed, name, Riz Ahmed]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Riz Ahmed Context triple: [Riz Ahmed, name, Riz Ahmed]
-
A.
Riz Ahmed
chosen
Riz Ahmed is a British actor and rapper acclaimed for his roles in films like "Nightcrawler," "Rogue One," and the Oscar-winning "The Long Goodbye."
-
B.
Adil Hussain
Adil Hussain is an Indian actor known for his nuanced performances in both Indian and international films, as well as in theatre and television.
-
C.
Mahershala Ali
Mahershala Ali is an American actor acclaimed for his powerful performances in films and television, earning multiple Academy Awards and widespread recognition for his nuanced, character-driven roles.
-
D.
Yahya Abdul-Mateen II
Yahya Abdul-Mateen II is an American actor known for his roles in major films and TV series such as "Aquaman," "Watchmen," and "Candyman."
-
E.
Omar Sy
Omar Sy is a French actor and comedian best known internationally for his breakout role in the film "The Intouchables" and subsequent work in movies like "Jurassic World" and the series "Lupin."
- 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_69ca83dc94ac8190b9ef42684d36ff39 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca8495c448190b9bb3803fb2dda70 |
completed | April 1, 2026, 5:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d03052716c8190835b0d3357a29ce5 |
completed | April 3, 2026, 9:25 p.m. |
Created at: March 30, 2026, 7:16 p.m.