Triple
T20417408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jhankaar Beats |
E500748
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object |
Suresh Pai
Suresh Pai is an Indian film editor best known for his work on the acclaimed Hindi musical comedy-drama "Jhankaar Beats."
|
E1430380
|
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: Suresh Pai | Statement: [Jhankaar Beats, editor, Suresh Pai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suresh Pai Context triple: [Jhankaar Beats, editor, Suresh Pai]
-
A.
Laxman Narasimhan
Laxman Narasimhan is an Indian-American business executive best known as the chief executive officer of Starbucks and former CEO of Reckitt Benckiser.
-
B.
Raj Jain
Raj Jain is a prominent computer scientist known for his influential contributions to computer networking and performance analysis.
-
C.
Vijay Joshi
Vijay Joshi is an Indian economist known for his influential work on macroeconomic policy and development, particularly in the context of the Indian economy.
-
D.
Sanjay Jain
Sanjay Jain is an economist recognized for his academic contributions and scholarship associated with the Delhi School of Economics.
-
E.
Vijay Bhalla
Vijay Bhalla is a person notable enough to be recognized as a bearer of the surname Bhalla, though specific widely known public details about him are not clearly established.
- 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: Suresh Pai Triple: [Jhankaar Beats, editor, Suresh Pai]
Generated description
Suresh Pai is an Indian film editor best known for his work on the acclaimed Hindi musical comedy-drama "Jhankaar Beats."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Suresh Pai Target entity description: Suresh Pai is an Indian film editor best known for his work on the acclaimed Hindi musical comedy-drama "Jhankaar Beats."
-
A.
Laxman Narasimhan
Laxman Narasimhan is an Indian-American business executive best known as the chief executive officer of Starbucks and former CEO of Reckitt Benckiser.
-
B.
Raj Jain
Raj Jain is a prominent computer scientist known for his influential contributions to computer networking and performance analysis.
-
C.
Vijay Joshi
Vijay Joshi is an Indian economist known for his influential work on macroeconomic policy and development, particularly in the context of the Indian economy.
-
D.
Sanjay Jain
Sanjay Jain is an economist recognized for his academic contributions and scholarship associated with the Delhi School of Economics.
-
E.
Vijay Bhalla
Vijay Bhalla is a person notable enough to be recognized as a bearer of the surname Bhalla, though specific widely known public details about him are not clearly established.
- 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_6a0883f006748190ac1c516e9623637e |
completed | May 16, 2026, 2:49 p.m. |
| NEDg | Description generation | batch_6a08844e244081909fe8b2dc54e3a8fc |
completed | May 16, 2026, 2:50 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0884b3fe308190b3d2e7e2db5c3493 |
completed | May 16, 2026, 2:52 p.m. |
Created at: April 16, 2026, 11:30 a.m.