Triple

T32162495
Position Surface form Disambiguated ID Type / Status
Subject R. Madhavan E821463 entity
Predicate notableWork P4 FINISHED
Object Rocketry: The Nambi Effect
Rocketry: The Nambi Effect is a 2022 Indian biographical drama film that chronicles the life and wrongful espionage case of ISRO scientist Nambi Narayanan.
E1996384 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: Rocketry: The Nambi Effect | Statement: [R. Madhavan, notableWork, Rocketry: The Nambi Effect]
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: Rocketry: The Nambi Effect
Triple: [R. Madhavan, notableWork, Rocketry: The Nambi Effect]
Generated description
Rocketry: The Nambi Effect is a 2022 Indian biographical drama film that chronicles the life and wrongful espionage case of ISRO scientist Nambi Narayanan.

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_69f34905e098819082191a6922a6d607 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6ba1de3548190b51d38e380bc67ef completed May 3, 2026, 2:59 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2f0bd27d80819092e258930938214b completed June 14, 2026, 8:15 p.m.
NEDg Description generation batch_6a2f0c47380881909610ef39ac0c5a79 completed June 14, 2026, 8:17 p.m.
NED2 Entity disambiguation (via description) batch_6a2f2eee843881909b1b90c96ec496fe completed June 14, 2026, 10:45 p.m.
Created at: May 1, 2026, 12:32 a.m.