Triple
T8565338
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baashha |
E202787
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object |
Balakumaran
Balakumaran was a prominent Indian Tamil author and screenwriter known for his popular novels and contributions to Tamil cinema.
|
E751883
|
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: Balakumaran | Statement: [Baashha, writer, Balakumaran]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Balakumaran Context triple: [Baashha, writer, Balakumaran]
-
A.
Duraimurugan
Duraimurugan is an Indian politician from Tamil Nadu and a senior leader of the Dravida Munnetra Kazhagam (DMK) party.
-
B.
Nandha
Nandha is a 2001 Tamil-language drama film directed by Bala, widely recognized for Suriya’s breakthrough performance in a gritty, emotionally intense role.
-
C.
Kothandaramar
Kothandaramar is a revered form of the Hindu god Rama, typically depicted holding a bow and associated with devotion, righteousness, and temple worship in South India.
-
D.
Anandaraj
Anandaraj is an Indian film actor best known for his villainous and character roles in Tamil cinema.
-
E.
Daswanth
Daswanth was a prominent 16th-century Mughal court painter known for his innovative and richly detailed miniature illustrations.
- 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: Balakumaran Triple: [Baashha, writer, Balakumaran]
Generated description
Balakumaran was a prominent Indian Tamil author and screenwriter known for his popular novels and contributions to Tamil cinema.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Balakumaran Target entity description: Balakumaran was a prominent Indian Tamil author and screenwriter known for his popular novels and contributions to Tamil cinema.
-
A.
Duraimurugan
Duraimurugan is an Indian politician from Tamil Nadu and a senior leader of the Dravida Munnetra Kazhagam (DMK) party.
-
B.
Nandha
Nandha is a 2001 Tamil-language drama film directed by Bala, widely recognized for Suriya’s breakthrough performance in a gritty, emotionally intense role.
-
C.
Kothandaramar
Kothandaramar is a revered form of the Hindu god Rama, typically depicted holding a bow and associated with devotion, righteousness, and temple worship in South India.
-
D.
Anandaraj
Anandaraj is an Indian film actor best known for his villainous and character roles in Tamil cinema.
-
E.
Daswanth
Daswanth was a prominent 16th-century Mughal court painter known for his innovative and richly detailed miniature illustrations.
- 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_69ca8327b0a881908606ff860713964d |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe9d2331881909d92ddde90f580e9 |
completed | March 31, 2026, 3:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cef3265be481909acfef718e2bd403 |
completed | April 2, 2026, 10:52 p.m. |
| NEDg | Description generation | batch_69cef52000048190bc5451cfb6446ced |
completed | April 2, 2026, 11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cef809df548190b4f9ecc709b3b065 |
completed | April 2, 2026, 11:13 p.m. |
Created at: March 30, 2026, 6:20 p.m.