Triple

T32024645
Position Surface form Disambiguated ID Type / Status
Subject Chetpet E817784 entity
Predicate hasHealthcareFacility P12416 FINISHED
Object Apollo First Med Hospitals
Apollo First Med Hospitals is a multi-specialty healthcare facility in Chetpet, Chennai, known for providing a range of modern medical and surgical services.
 E1988499 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: Apollo First Med Hospitals | Statement: [Chetpet, hasHealthcareFacility, Apollo First Med Hospitals]
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: Apollo First Med Hospitals
Triple: [Chetpet, hasHealthcareFacility, Apollo First Med Hospitals]
Generated description
Apollo First Med Hospitals is a multi-specialty healthcare facility in Chetpet, Chennai, known for providing a range of modern medical and surgical services.

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_69f348fb04e4819081f4eab040ed7959 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6b4694e6c81908bf4f730ddab3fbf completed May 3, 2026, 2:35 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2ed4edc49c8190954488a518693cf4 completed June 14, 2026, 4:21 p.m.
NEDg Description generation batch_6a2ed627b97481908e0d618ba90fb9ec completed June 14, 2026, 4:26 p.m.
NED2 Entity disambiguation (via description) batch_6a2ed6f938548190b1151d9ee583f82b completed June 14, 2026, 4:29 p.m.
Created at: May 1, 2026, 12:17 a.m.