Triple
T10445113
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ali Fazal |
E246266
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Ali Fazal |
E246266
|
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: Ali Fazal | Statement: [Ali Fazal, name, Ali Fazal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ali Fazal Context triple: [Ali Fazal, name, Ali Fazal]
-
A.
Ali Fazal
chosen
Ali Fazal is an Indian actor known for his work in both Bollywood and international productions, including roles in films like "Victoria & Abdul" and the series "Mirzapur."
-
B.
Manish Bhasin
Manish Bhasin is a British sports journalist and television presenter best known for his long-running work on BBC football coverage.
-
C.
David Dhawan
David Dhawan is a prominent Indian film director best known for his popular Bollywood comedy films, especially those starring Govinda in the 1990s and early 2000s.
-
D.
Rajkummar Rao
Rajkummar Rao is an acclaimed Indian film actor known for his versatile performances in Hindi cinema, particularly in critically praised independent and mainstream films.
-
E.
Shahid Kapoor
Shahid Kapoor is a popular Indian film actor known for his versatile performances in Hindi cinema, including acclaimed roles in films like "Jab We Met," "Haider," and "Kabir Singh."
- 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_69d381c04fe08190957c26c526a3b05a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4fdbf81508190a160edea85105d3a |
completed | April 7, 2026, 12:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d9987838ac8190a6ba09305fc27621 |
completed | April 11, 2026, 12:40 a.m. |
Created at: April 6, 2026, 12:16 p.m.