Triple
T8565264
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | J. Jayalalithaa |
E202785
|
entity |
| Predicate | industry |
P71
|
FINISHED |
| Object | Tamil cinema |
E39755
|
NE FINISHED |
How this triple was built (2 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: Tamil cinema | Statement: [J. Jayalalithaa, industry, Tamil cinema]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tamil cinema Context triple: [J. Jayalalithaa, industry, Tamil cinema]
-
A.
Tamil cinema
chosen
Tamil cinema is the film industry based in the Indian state of Tamil Nadu, primarily producing Tamil-language movies and known for its influential contributions to Indian and global cinema.
-
B.
Pollywood
Pollywood is the regional film industry based in the Indian state of Punjab, producing Punjabi-language movies and entertainment content.
-
C.
Kollywood
Kollywood is the Tamil-language film industry based in Chennai, India, known for its prolific output of commercial and artistic cinema.
-
D.
Mollywood
Mollywood is the Malayalam-language film industry based in the Indian state of Kerala, known for its content-driven cinema and strong storytelling traditions.
-
E.
Tollywood
Tollywood is the Bengali-language film industry based primarily in Kolkata, India, known for its rich artistic and literary cinematic tradition.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d05bdc87fc8190a5ae0883742cb08c |
completed | April 4, 2026, 12:31 a.m. |
Created at: March 30, 2026, 6:20 p.m.