Triple

T28295402
Position Surface form Disambiguated ID Type / Status
Subject BOEC E713555 entity
Predicate fullName P16 FINISHED
Object Bureau of Emergency Communications
The Bureau of Emergency Communications is a public safety agency responsible for receiving and dispatching emergency and non-emergency calls to police, fire, and medical services within its jurisdiction.
E1812429 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: Bureau of Emergency Communications | Statement: [BOEC, fullName, Bureau of Emergency Communications]
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: Bureau of Emergency Communications
Triple: [BOEC, fullName, Bureau of Emergency Communications]
Generated description
The Bureau of Emergency Communications is a public safety agency responsible for receiving and dispatching emergency and non-emergency calls to police, fire, and medical services within its jurisdiction.

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_69efb524ab688190a1ce7ee7c9520932 completed April 27, 2026, 7:12 p.m.
NER Named-entity recognition batch_69f644ad5bc48190b79ce1c249863166 completed May 2, 2026, 6:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a16073162dc8190b442e08f4a17e37b completed May 26, 2026, 8:48 p.m.
NEDg Description generation batch_6a1613bfd9bc8190a976e350dc9373d7 completed May 26, 2026, 9:42 p.m.
NED2 Entity disambiguation (via description) batch_6a1614dc78b48190a1a5d5832b7fa518 completed May 26, 2026, 9:47 p.m.
Created at: April 27, 2026, 11:32 p.m.