Triple
T21428515
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Thursday |
E528622
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object |
Anuj Rakesh Dhawan
Anuj Rakesh Dhawan is an Indian cinematographer known for his work on Hindi films, including the thriller "A Thursday."
|
E1485226
|
NE FINISHED |
How this triple was built (4 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: Anuj Rakesh Dhawan | Statement: [A Thursday, cinematographyBy, Anuj Rakesh Dhawan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anuj Rakesh Dhawan Context triple: [A Thursday, cinematographyBy, Anuj Rakesh Dhawan]
-
A.
Rajeev Singh
Rajeev Singh is a technology entrepreneur best known as a co-founder and former executive leader of the travel and expense management company Concur Technologies.
-
B.
Vijay Maurya
Vijay Maurya is an Indian actor, writer, and director known for his work in Hindi cinema and web series.
-
C.
Rajeev Shukla
Rajeev Shukla is an Indian politician, journalist, and cricket administrator known for his long-standing involvement with the Board of Control for Cricket in India (BCCI) and the Indian Premier League (IPL).
-
D.
Vijay Arora
Vijay Arora is a cinematographer known for his work on the Hindi film "Shaadi No. 1."
-
E.
Mukul Sharma
Mukul Sharma was an Indian writer, journalist, and science fiction author known for his popular science columns and for inspiring several acclaimed film adaptations.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Anuj Rakesh Dhawan Triple: [A Thursday, cinematographyBy, Anuj Rakesh Dhawan]
Generated description
Anuj Rakesh Dhawan is an Indian cinematographer known for his work on Hindi films, including the thriller "A Thursday."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anuj Rakesh Dhawan Target entity description: Anuj Rakesh Dhawan is an Indian cinematographer known for his work on Hindi films, including the thriller "A Thursday."
-
A.
Rajeev Singh
Rajeev Singh is a technology entrepreneur best known as a co-founder and former executive leader of the travel and expense management company Concur Technologies.
-
B.
Vijay Maurya
Vijay Maurya is an Indian actor, writer, and director known for his work in Hindi cinema and web series.
-
C.
Rajeev Shukla
Rajeev Shukla is an Indian politician, journalist, and cricket administrator known for his long-standing involvement with the Board of Control for Cricket in India (BCCI) and the Indian Premier League (IPL).
-
D.
Vijay Arora
Vijay Arora is a cinematographer known for his work on the Hindi film "Shaadi No. 1."
-
E.
Mukul Sharma
Mukul Sharma was an Indian writer, journalist, and science fiction author known for his popular science columns and for inspiring several acclaimed film adaptations.
- F. None of above. chosen
Provenance (5 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_69e0c455f3688190810bc96365791b0f |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b3e74bcc81909ad66e3c59152ffc |
completed | April 22, 2026, 11:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09c8f97f7481909d4b7fc1bc43f0f1 |
completed | May 17, 2026, 1:56 p.m. |
| NEDg | Description generation | batch_6a09c9a6b84881908f9193b89ded7361 |
completed | May 17, 2026, 1:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09caa391588190808eb3dcaff07803 |
completed | May 17, 2026, 2:03 p.m. |
Created at: April 16, 2026, 5:49 p.m.