Triple
T21109490
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mollywood |
E520135
|
entity |
| Predicate | hasNotableActor |
P17435
|
FINISHED |
| Object | Dileep |
—
|
NE NERFINISHED |
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: Dileep | Statement: [Mollywood, hasNotableActor, Dileep]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dileep Context triple: [Mollywood, hasNotableActor, Dileep]
-
A.
Dileep
chosen
Dileep is an Indian film actor and producer best known for his work in Malayalam cinema.
-
B.
Madhavan
Madhavan is an Indian film actor best known for his work in Hindi and Tamil cinema, including a notable role in the acclaimed film "Rang De Basanti."
-
C.
Prithviraj Sukumaran
Prithviraj Sukumaran is an acclaimed Indian film actor, producer, and director primarily known for his work in Malayalam cinema, with notable performances across multiple South Indian and Hindi films.
-
D.
Paresh Babu
Paresh Babu is a central fictional character in Rabindranath Tagore’s Bengali novel "Gora," representing complex social and philosophical themes in colonial India.
-
E.
Prakash Raj
Prakash Raj is an acclaimed Indian actor, filmmaker, and producer known for his versatile performances across multiple South Indian film industries and Hindi cinema.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b509a318819092fbbcb21d1fe603 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7210110a48190a6359b6732f6293d |
completed | April 21, 2026, 7:02 a.m. |
Created at: April 16, 2026, 2:54 p.m.