Triple

T24679423
Position Surface form Disambiguated ID Type / Status
Subject Calvert Library Twin Beaches Branch (serving area) E611084 entity
Predicate partOf P40 FINISHED
Object Calvert Library system
The Calvert Library system is a public library network serving residents of Calvert County, Maryland through multiple community branches and services.
E1646394 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: Calvert Library system | Statement: [Calvert Library Twin Beaches Branch (serving area), partOf, Calvert Library system]
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: Calvert Library system
Triple: [Calvert Library Twin Beaches Branch (serving area), partOf, Calvert Library system]
Generated description
The Calvert Library system is a public library network serving residents of Calvert County, Maryland through multiple community branches and services.

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_69e2c4d5c2dc8190ac857dea25ec6ce9 completed April 17, 2026, 11:40 p.m.
NER Named-entity recognition batch_69f40fbf68d48190b92e809a8947d60e completed May 1, 2026, 2:28 a.m.
NED1 Entity disambiguation (via context triple) batch_6a100ffa74808190a000df2e92e438fc completed May 22, 2026, 8:12 a.m.
NEDg Description generation batch_6a10136992b481909ee04d5c09867f21 completed May 22, 2026, 8:27 a.m.
NED2 Entity disambiguation (via description) batch_6a10141161b08190b471a7882a4d8aa0 completed May 22, 2026, 8:30 a.m.
Created at: April 18, 2026, 3:08 a.m.