Triple

T27208613
Position Surface form Disambiguated ID Type / Status
Subject Chhapaak E683939 entity
Predicate writer P1360 FINISHED
Object Atika Chohan
Atika Chohan is an Indian screenwriter best known for co-writing impactful Hindi films such as "Chhapaak" and "Margarita with a Straw."
E1769683 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: Atika Chohan | Statement: [Chhapaak, writer, Atika Chohan]
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: Atika Chohan
Triple: [Chhapaak, writer, Atika Chohan]
Generated description
Atika Chohan is an Indian screenwriter best known for co-writing impactful Hindi films such as "Chhapaak" and "Margarita with a Straw."

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_69eefad339a08190aeacb2a198f1a39b completed April 27, 2026, 5:57 a.m.
NER Named-entity recognition batch_69f625e6cd708190aea9dc220df25717 completed May 2, 2026, 4:27 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12a7bf54308190a5237bf8f6419a4b completed May 24, 2026, 7:24 a.m.
NEDg Description generation batch_6a12a85d95248190b4aa8bcc6c182b35 completed May 24, 2026, 7:27 a.m.
NED2 Entity disambiguation (via description) batch_6a12aa051060819082b52092cdccd0d5 completed May 24, 2026, 7:34 a.m.
Created at: April 27, 2026, 9:38 a.m.