Triple

T28159229
Position Surface form Disambiguated ID Type / Status
Subject Bidhannagar College E714841 entity
Predicate hasDepartment P35 FINISHED
Object Department of Political Science
The Department of Political Science is an academic unit at Bidhannagar College dedicated to teaching and research on government, politics, public policy, and political theory.
E1804968 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: Department of Political Science | Statement: [Bidhannagar College, hasDepartment, Department of Political Science]
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: Department of Political Science
Triple: [Bidhannagar College, hasDepartment, Department of Political Science]
Generated description
The Department of Political Science is an academic unit at Bidhannagar College dedicated to teaching and research on government, politics, public policy, and political theory.

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_69efd6b156448190bfa15958208395c3 completed April 27, 2026, 9:35 p.m.
NER Named-entity recognition batch_69f641e9eac08190976874fc569b4a63 completed May 2, 2026, 6:26 p.m.
NED1 Entity disambiguation (via context triple) batch_6a15d7b33a0081909bf7bff24b859055 completed May 26, 2026, 5:26 p.m.
NEDg Description generation batch_6a15d95d6bdc81909e7388b148315629 completed May 26, 2026, 5:33 p.m.
NED2 Entity disambiguation (via description) batch_6a15d9dd60b88190a4c74a6f7cd1217e completed May 26, 2026, 5:35 p.m.
Created at: April 27, 2026, 10:05 p.m.