Triple
T19273491
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mandi |
E481986
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object |
Virendra Saini
Virendra Saini is an Indian cinematographer best known for his work on acclaimed parallel cinema films such as "Mandi."
|
E1370416
|
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: Virendra Saini | Statement: [Mandi, cinematographyBy, Virendra Saini]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Virendra Saini Context triple: [Mandi, cinematographyBy, Virendra Saini]
-
A.
Nayab Singh Saini
Nayab Singh Saini is an Indian politician from the Bharatiya Janata Party who serves as the Chief Minister of the state of Haryana.
-
B.
Nayab Singh Saini
Nayab Singh Saini is an Indian politician associated with the Bharatiya Janata Party who has served as a Member of Parliament and held key political roles in Haryana.
-
C.
Virendra Sharma
Virendra Sharma is a British Labour Party politician who has served as the Member of Parliament for the London constituency of Ealing Southall.
-
D.
Bharat Singh
Bharat Singh is a film editor known for his work on the popular Hindi movie "Raja Hindustani."
-
E.
Vijay Kumar
Vijay Kumar is a prominent roboticist and engineer known for his pioneering work in multi-robot systems and aerial robotics.
- 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: Virendra Saini Triple: [Mandi, cinematographyBy, Virendra Saini]
Generated description
Virendra Saini is an Indian cinematographer best known for his work on acclaimed parallel cinema films such as "Mandi."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Virendra Saini Target entity description: Virendra Saini is an Indian cinematographer best known for his work on acclaimed parallel cinema films such as "Mandi."
-
A.
Nayab Singh Saini
Nayab Singh Saini is an Indian politician from the Bharatiya Janata Party who serves as the Chief Minister of the state of Haryana.
-
B.
Nayab Singh Saini
Nayab Singh Saini is an Indian politician associated with the Bharatiya Janata Party who has served as a Member of Parliament and held key political roles in Haryana.
-
C.
Virendra Sharma
Virendra Sharma is a British Labour Party politician who has served as the Member of Parliament for the London constituency of Ealing Southall.
-
D.
Bharat Singh
Bharat Singh is a film editor known for his work on the popular Hindi movie "Raja Hindustani."
-
E.
Vijay Kumar
Vijay Kumar is a prominent roboticist and engineer known for his pioneering work in multi-robot systems and aerial robotics.
- 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_69d8e8ce54cc8190998418ff1f66ef28 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fbba7758819081c1c78667c59c5e |
completed | April 20, 2026, 10:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a071bce5ef08190bca61ea94b5956d5 |
completed | May 15, 2026, 1:12 p.m. |
| NEDg | Description generation | batch_6a071ca19f308190ac9072c5a8d3b665 |
completed | May 15, 2026, 1:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a071de4a7688190a7114a7eec39fd8f |
completed | May 15, 2026, 1:21 p.m. |
Created at: April 10, 2026, 1:29 p.m.