Triple

T33019287
Position Surface form Disambiguated ID Type / Status
Subject Central University of Tamil Nadu E844859 entity
Predicate hasDivision P35 FINISHED
Object School of Legal Studies
The School of Legal Studies is an academic division of the Central University of Tamil Nadu that offers legal education and research programs in law and related fields.
E1891928 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: School of Legal Studies | Statement: [Central University of Tamil Nadu, hasDivision, School of Legal Studies]
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: School of Legal Studies
Triple: [Central University of Tamil Nadu, hasDivision, School of Legal Studies]
Generated description
The School of Legal Studies is an academic division of the Central University of Tamil Nadu that offers legal education and research programs in law and related fields.

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_69f3494f3b4081909dccf2af34372a26 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d2aec45881909c0072e639fdeafd completed May 3, 2026, 4:44 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34e5087a948190aba6b41b46b0af8a completed June 19, 2026, 6:43 a.m.
NEDg Description generation batch_6a34e62111ec81908bf35fcc167b5a65 completed June 19, 2026, 6:48 a.m.
NED2 Entity disambiguation (via description) batch_6a34e6d3261c81908ab8544cc644b03a completed June 19, 2026, 6:50 a.m.
Created at: May 1, 2026, 1:23 a.m.