Triple

T31090540
Position Surface form Disambiguated ID Type / Status
Subject Drum Point, Maryland E792371 entity
Predicate roadAccessVia P9041 FINISHED
Object Maryland Route 760
Maryland Route 760 is a short state highway in Calvert County that connects the community of Drum Point with nearby major routes and surrounding areas in southern Maryland.
 E2124788 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: Maryland Route 760 | Statement: [Drum Point, Maryland, roadAccessVia, Maryland Route 760]
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: Maryland Route 760
Triple: [Drum Point, Maryland, roadAccessVia, Maryland Route 760]
Generated description
Maryland Route 760 is a short state highway in Calvert County that connects the community of Drum Point with nearby major routes and surrounding areas in southern Maryland.

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_69f224ce48348190bd0fc23f656ed683 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f6966aedf88190be0a2772bd484720 completed May 3, 2026, 12:27 a.m.
NED1 Entity disambiguation (via context triple) batch_6a37c60e47588190ab5d9ee2cce0b532 completed June 21, 2026, 11:07 a.m.
NEDg Description generation batch_6a37c6e970f48190b35c179e766c58cc completed June 21, 2026, 11:11 a.m.
NED2 Entity disambiguation (via description) batch_6a37cad6f71c81908794928c0e20ab20 completed June 21, 2026, 11:28 a.m.
Created at: April 29, 2026, 9:02 p.m.