Triple

T25333988
Position Surface form Disambiguated ID Type / Status
Subject Assam Police E635225 entity
Predicate hasDivision P35 FINISHED
Object Special Branch Assam Police
Special Branch Assam Police is the intelligence and security wing of the Assam Police responsible for gathering, analyzing, and disseminating information related to internal security and law and order in the state.
E635225 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: Special Branch Assam Police | Statement: [Assam Police, hasDivision, Special Branch Assam Police]
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: Special Branch Assam Police
Triple: [Assam Police, hasDivision, Special Branch Assam Police]
Generated description
Special Branch Assam Police is the intelligence and security wing of the Assam Police responsible for gathering, analyzing, and disseminating information related to internal security and law and order in the state.

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_69e75a99bd6481909476115b35b9a8e4 completed April 21, 2026, 11:08 a.m.
NER Named-entity recognition batch_69f497c80e0881908227999fa2c4e0d6 completed May 1, 2026, 12:08 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1075f0e95c81909720f8c5f3d01b5e completed May 22, 2026, 3:27 p.m.
NEDg Description generation batch_6a10775af30c8190b81d59d29bf57a2e completed May 22, 2026, 3:33 p.m.
NED2 Entity disambiguation (via description) batch_6a1078eaf8888190b3453537d13d6cc5 completed May 22, 2026, 3:40 p.m.
Created at: April 21, 2026, 1:31 p.m.