Triple
T19273442
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kalyug |
E481985
|
entity |
| Predicate | artDirectionBy |
P7743
|
FINISHED |
| Object |
Nitish Roy
Nitish Roy is an Indian art director and production designer known for his work on numerous acclaimed films and television projects.
|
E1367591
|
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: Nitish Roy | Statement: [Kalyug, artDirectionBy, Nitish Roy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nitish Roy Context triple: [Kalyug, artDirectionBy, Nitish Roy]
-
A.
Nitish Kumar
Nitish Kumar is an Indian politician who has served multiple terms as Chief Minister of Bihar and is a prominent leader in regional and national politics.
-
B.
Gyan Patnaik
Gyan Patnaik was an Indian aviator and the wife of prominent politician and former Odisha Chief Minister Biju Patnaik.
-
C.
Jyoti Basu
Jyoti Basu was a prominent Indian communist leader and one of the country’s longest-serving chief ministers, known for his pivotal role in West Bengal’s Left Front government.
-
D.
Babul Supriyo
Babul Supriyo is an Indian playback singer and politician known for his work in Bollywood films and Hindi music.
-
E.
Pranab Sen
Pranab Sen is an Indian physicist and academic known for his contributions to statistical physics and complex systems.
- 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: Nitish Roy Triple: [Kalyug, artDirectionBy, Nitish Roy]
Generated description
Nitish Roy is an Indian art director and production designer known for his work on numerous acclaimed films and television projects.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nitish Roy Target entity description: Nitish Roy is an Indian art director and production designer known for his work on numerous acclaimed films and television projects.
-
A.
Nitish Kumar
Nitish Kumar is an Indian politician who has served multiple terms as Chief Minister of Bihar and is a prominent leader in regional and national politics.
-
B.
Gyan Patnaik
Gyan Patnaik was an Indian aviator and the wife of prominent politician and former Odisha Chief Minister Biju Patnaik.
-
C.
Jyoti Basu
Jyoti Basu was a prominent Indian communist leader and one of the country’s longest-serving chief ministers, known for his pivotal role in West Bengal’s Left Front government.
-
D.
Babul Supriyo
Babul Supriyo is an Indian playback singer and politician known for his work in Bollywood films and Hindi music.
-
E.
Pranab Sen
Pranab Sen is an Indian physicist and academic known for his contributions to statistical physics and complex systems.
- 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_6a070e748ba48190bc2a17906f7ad842 |
completed | May 15, 2026, 12:15 p.m. |
| NEDg | Description generation | batch_6a070ef513888190b8c9f8c2de36f234 |
completed | May 15, 2026, 12:17 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a070fe766d081909453b1f8563817e8 |
completed | May 15, 2026, 12:21 p.m. |
Created at: April 10, 2026, 1:29 p.m.