Triple

T27399066
Position Surface form Disambiguated ID Type / Status
Subject Maryland Route 80 E691778 entity
Predicate servesCommunity P82 FINISHED
Object Linganore area
The Linganore area is a residential and semi-rural community in Frederick County, Maryland, known for its planned neighborhoods, schools, and proximity to Lake Linganore.
E1770640 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: Linganore area | Statement: [Maryland Route 80, servesCommunity, Linganore area]
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: Linganore area
Triple: [Maryland Route 80, servesCommunity, Linganore area]
Generated description
The Linganore area is a residential and semi-rural community in Frederick County, Maryland, known for its planned neighborhoods, schools, and proximity to Lake Linganore.

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_69ef5204f7048190bf226a129858fc5b completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62cb258ec8190834d9f98cd772779 completed May 2, 2026, 4:56 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12a7f877188190a608a71afafa46e0 completed May 24, 2026, 7:25 a.m.
NEDg Description generation batch_6a12a91cda148190b9d85ae8f9d24250 completed May 24, 2026, 7:30 a.m.
NED2 Entity disambiguation (via description) batch_6a12aa0a89b88190ad4e1c5b0e26205b completed May 24, 2026, 7:34 a.m.
Created at: April 27, 2026, 12:28 p.m.