Triple

T36334595
Position Surface form Disambiguated ID Type / Status
Subject Faculty of Medical Sciences, University of the West Indies, Mona E894745 entity
Predicate acronym P43 FINISHED
Object FMS
FMS is the Faculty of Medical Sciences at the University of the West Indies’ Mona campus, responsible for training healthcare professionals and conducting medical research in the Caribbean region.
E2179257 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: FMS | Statement: [Faculty of Medical Sciences, University of the West Indies, Mona, acronym, FMS]
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: FMS
Triple: [Faculty of Medical Sciences, University of the West Indies, Mona, acronym, FMS]
Generated description
FMS is the Faculty of Medical Sciences at the University of the West Indies’ Mona campus, responsible for training healthcare professionals and conducting medical research in the Caribbean region.

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_69f76e4e90148190b02fe52593c70b5b completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7ba71682c81908b4ef99876e54cb0 completed May 3, 2026, 9:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a397d9b09bc8190a76ee1f70c473b5b completed June 22, 2026, 6:23 p.m.
NEDg Description generation batch_6a398d05ec64819094a1ddb66ad32128 completed June 22, 2026, 7:29 p.m.
NED2 Entity disambiguation (via description) batch_6a398e1a986881908c6727fd6a03cffd completed June 22, 2026, 7:33 p.m.
Created at: May 3, 2026, 4:09 p.m.