Triple
T8559092
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vikram (2022 film) |
E202645
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object |
Narain
Narain is an Indian actor known for his work in Tamil and Malayalam cinema, often appearing in action and thriller films.
|
E743921
|
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: Narain | Statement: [Vikram (2022 film), stars, Narain]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Narain Context triple: [Vikram (2022 film), stars, Narain]
-
A.
Vaz Pinto
Vaz Pinto is a Portuguese surname associated with figures such as Catarina de Almeida Vaz Pinto, a notable cultural and political personality in Portugal.
-
B.
Bharat Arun
Bharat Arun is a former Indian cricketer and renowned fast-bowling coach who has played a key role in developing several of India's leading pace bowlers.
-
C.
Ajay Naidu
Ajay Naidu is an American actor best known for his roles in independent films and the cult comedy "Office Space."
-
D.
Arun
Arun is a local government district and borough in West Sussex, England, named after the River Arun and encompassing coastal towns such as Bognor Regis and Littlehampton.
-
E.
Tony Fernandes
Tony Fernandes is a Malaysian entrepreneur best known as the founder of AirAsia and a prominent football executive and former chairman of Queens Park Rangers F.C.
- 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: Narain Triple: [Vikram (2022 film), stars, Narain]
Generated description
Narain is an Indian actor known for his work in Tamil and Malayalam cinema, often appearing in action and thriller films.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Narain Target entity description: Narain is an Indian actor known for his work in Tamil and Malayalam cinema, often appearing in action and thriller films.
-
A.
Vaz Pinto
Vaz Pinto is a Portuguese surname associated with figures such as Catarina de Almeida Vaz Pinto, a notable cultural and political personality in Portugal.
-
B.
Bharat Arun
Bharat Arun is a former Indian cricketer and renowned fast-bowling coach who has played a key role in developing several of India's leading pace bowlers.
-
C.
Ajay Naidu
Ajay Naidu is an American actor best known for his roles in independent films and the cult comedy "Office Space."
-
D.
Arun
Arun is a local government district and borough in West Sussex, England, named after the River Arun and encompassing coastal towns such as Bognor Regis and Littlehampton.
-
E.
Tony Fernandes
Tony Fernandes is a Malaysian entrepreneur best known as the founder of AirAsia and a prominent football executive and former chairman of Queens Park Rangers F.C.
- 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_69ca8326e6c881908ff720d6abaebdc5 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe9485dd88190bc2cf2adf39d48ee |
completed | March 31, 2026, 3:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce894d82588190b558d5b2dc65eafe |
completed | April 2, 2026, 3:20 p.m. |
| NEDg | Description generation | batch_69ce8a9ce1a08190a579f7f7a0319d01 |
completed | April 2, 2026, 3:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ce8bdf1f148190ac832424661bd8e5 |
completed | April 2, 2026, 3:31 p.m. |
Created at: March 30, 2026, 6:20 p.m.