Triple
T20417576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shaadi No. 1 |
E500751
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object |
Vijay Arora
Vijay Arora is a cinematographer known for his work on the Hindi film "Shaadi No. 1."
|
E1447933
|
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: Vijay Arora | Statement: [Shaadi No. 1, cinematographyBy, Vijay Arora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vijay Arora Context triple: [Shaadi No. 1, cinematographyBy, Vijay Arora]
-
A.
Vijay Maurya
Vijay Maurya is an Indian actor, writer, and director known for his work in Hindi cinema and web series.
-
B.
Rajeev Samant
Rajeev Samant is an Indian entrepreneur best known as the pioneering founder of Sula Vineyards, one of India’s largest and most influential wine producers.
-
C.
Ashok Arora
Ashok Arora is a person whose specific public background or notable achievements are not clearly identifiable from the given information.
-
D.
Ashok Arora
Ashok Arora is an Indian entrepreneur best known as one of the co-founders of the global IT services and consulting company Infosys.
-
E.
Vipin Sharma
Vipin Sharma is an Indian actor known for his character roles in Hindi cinema and streaming projects, including a prominent part in the action thriller film "Monkey Man."
- 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: Vijay Arora Triple: [Shaadi No. 1, cinematographyBy, Vijay Arora]
Generated description
Vijay Arora is a cinematographer known for his work on the Hindi film "Shaadi No. 1."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vijay Arora Target entity description: Vijay Arora is a cinematographer known for his work on the Hindi film "Shaadi No. 1."
-
A.
Vijay Maurya
Vijay Maurya is an Indian actor, writer, and director known for his work in Hindi cinema and web series.
-
B.
Rajeev Samant
Rajeev Samant is an Indian entrepreneur best known as the pioneering founder of Sula Vineyards, one of India’s largest and most influential wine producers.
-
C.
Ashok Arora
Ashok Arora is a person whose specific public background or notable achievements are not clearly identifiable from the given information.
-
D.
Ashok Arora
Ashok Arora is an Indian entrepreneur best known as one of the co-founders of the global IT services and consulting company Infosys.
-
E.
Vipin Sharma
Vipin Sharma is an Indian actor known for his character roles in Hindi cinema and streaming projects, including a prominent part in the action thriller film "Monkey Man."
- 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_69e0b4a935588190b9446a99b37ced44 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67a44ecf48190ba5a3872af500dc8 |
completed | April 20, 2026, 7:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08e8202e748190a043ac51dc8faa01 |
completed | May 16, 2026, 9:56 p.m. |
| NEDg | Description generation | batch_6a08e88b1e1c8190a02a52e50470ce79 |
completed | May 16, 2026, 9:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08e923a134819082e8d677c420f8ce |
completed | May 16, 2026, 10:01 p.m. |
Created at: April 16, 2026, 11:30 a.m.