Triple

T30936647
Position Surface form Disambiguated ID Type / Status
Subject Ramakrishna movement E788139 entity
Predicate prominentFigure P643 FINISHED
Object Swami Akhandananda
Swami Akhandananda was a direct disciple of Sri Ramakrishna and a pioneering monk of the Ramakrishna Order, known especially for his extensive relief and social service work among the poor in India.
E1949093 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: Swami Akhandananda | Statement: [Ramakrishna movement, prominentFigure, Swami Akhandananda]
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: Swami Akhandananda
Triple: [Ramakrishna movement, prominentFigure, Swami Akhandananda]
Generated description
Swami Akhandananda was a direct disciple of Sri Ramakrishna and a pioneering monk of the Ramakrishna Order, known especially for his extensive relief and social service work among the poor in India.

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_69f224c0b7fc819090cb89df60d23653 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f692e5069c81908013503ba8d065d7 completed May 3, 2026, 12:12 a.m.
NED1 Entity disambiguation (via context triple) batch_6a294707bad48190b51620ec9d708e88 completed June 10, 2026, 11:14 a.m.
NEDg Description generation batch_6a2947cb99048190b349aa52120b1e24 completed June 10, 2026, 11:17 a.m.
NED2 Entity disambiguation (via description) batch_6a2948bb63e4819083a1e9d149cddac6 completed June 10, 2026, 11:21 a.m.
Created at: April 29, 2026, 8:52 p.m.