Triple

T29709762
Position Surface form Disambiguated ID Type / Status
Subject Billa II E751745 entity
Predicate hasCastMember P2308 FINISHED
Object Dinesh Lamba
Dinesh Lamba is an Indian film actor known for his supporting and character roles in various regional and Bollywood movies.
E1971988 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: Dinesh Lamba | Statement: [Billa II, hasCastMember, Dinesh Lamba]
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: Dinesh Lamba
Triple: [Billa II, hasCastMember, Dinesh Lamba]
Generated description
Dinesh Lamba is an Indian film actor known for his supporting and character roles in various regional and Bollywood movies.

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_69f0d62748848190b030d0a703629a7d completed April 28, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f672d8463481909f9144dbfa8be162 completed May 2, 2026, 9:55 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2b79a86f0081908164df3f6099316c completed June 12, 2026, 3:14 a.m.
NEDg Description generation batch_6a2b7a866a408190a377ebe1dfb4b162 completed June 12, 2026, 3:18 a.m.
NED2 Entity disambiguation (via description) batch_6a2b7b5699c48190b83c080aa685a7b4 completed June 12, 2026, 3:21 a.m.
Created at: April 28, 2026, 7:30 p.m.