Triple

T29893432
Position Surface form Disambiguated ID Type / Status
Subject Jagadguru Kripalu Parishat E759214 entity
Predicate operates P24 FINISHED
Object Jagadguru Kripalu Hospital
Jagadguru Kripalu Hospital is a charitable healthcare facility in India providing low-cost or free medical services, particularly to underserved rural and economically disadvantaged populations.
E1892188 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: Jagadguru Kripalu Hospital | Statement: [Jagadguru Kripalu Parishat, operates, Jagadguru Kripalu Hospital]
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: Jagadguru Kripalu Hospital
Triple: [Jagadguru Kripalu Parishat, operates, Jagadguru Kripalu Hospital]
Generated description
Jagadguru Kripalu Hospital is a charitable healthcare facility in India providing low-cost or free medical services, particularly to underserved rural and economically disadvantaged populations.

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_69f2245f1cf88190978c70d1a1d2cb73 completed April 29, 2026, 3:31 p.m.
NER Named-entity recognition batch_69f67728a5208190a43626fb4f5669c7 completed May 2, 2026, 10:14 p.m.
NED1 Entity disambiguation (via context triple) batch_6a271410c1d88190996ab0ff68d9b283 completed June 8, 2026, 7:12 p.m.
NEDg Description generation batch_6a2714e0a8e48190bcebb7601fc3b4e8 completed June 8, 2026, 7:15 p.m.
NED2 Entity disambiguation (via description) batch_6a271968eae08190834668e2da3cd703 completed June 8, 2026, 7:35 p.m.
Created at: April 29, 2026, 6:03 p.m.