Triple

T28761471
Position Surface form Disambiguated ID Type / Status
Subject Baltimore Police Department E726125 entity
Predicate hasDivision P35 FINISHED
Object Special Operations Section
The Special Operations Section is a specialized unit within the Baltimore Police Department responsible for handling high-risk, tactical, and specialized law enforcement operations.
E1833728 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 Operations Section | Statement: [Baltimore Police Department, hasDivision, Special Operations Section]
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 Operations Section
Triple: [Baltimore Police Department, hasDivision, Special Operations Section]
Generated description
The Special Operations Section is a specialized unit within the Baltimore Police Department responsible for handling high-risk, tactical, and specialized law enforcement operations.

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_69f03198be14819098fa74e48b3749bf completed April 28, 2026, 4:03 a.m.
NER Named-entity recognition batch_69f657fffee88190b4354875b78e0a7e completed May 2, 2026, 8:01 p.m.
NED1 Entity disambiguation (via context triple) batch_6a24a25d64c4819087213e5c37c56a54 completed June 6, 2026, 10:42 p.m.
NEDg Description generation batch_6a24a667d6d08190917858826b13e134 completed June 6, 2026, 10:59 p.m.
NED2 Entity disambiguation (via description) batch_6a24aaa014a081908831ccb241673fae completed June 6, 2026, 11:17 p.m.
Created at: April 28, 2026, 6:12 a.m.