Triple

T27166555
Position Surface form Disambiguated ID Type / Status
Subject Mohan Bhargava E682794 entity
Predicate themeAssociated P2830 FINISHED
Object Reverse brain drain
Reverse brain drain is the phenomenon in which skilled professionals who had emigrated abroad return to their home country, bringing back expertise, capital, and innovation.
E1762114 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: Reverse brain drain | Statement: [Mohan Bhargava, themeAssociated, Reverse brain drain]
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: Reverse brain drain
Triple: [Mohan Bhargava, themeAssociated, Reverse brain drain]
Generated description
Reverse brain drain is the phenomenon in which skilled professionals who had emigrated abroad return to their home country, bringing back expertise, capital, and innovation.

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_69eefacf6e788190a75a64399d9e3109 completed April 27, 2026, 5:57 a.m.
NER Named-entity recognition batch_69f62543fd78819093ccb2b5844dfd72 completed May 2, 2026, 4:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1253853b648190a75e7a57181f5e9b completed May 24, 2026, 1:25 a.m.
NEDg Description generation batch_6a12545544f881909f0afd8459986559 completed May 24, 2026, 1:28 a.m.
NED2 Entity disambiguation (via description) batch_6a125879112c8190959380eaef8ccf19 completed May 24, 2026, 1:46 a.m.
Created at: April 27, 2026, 9:21 a.m.