Triple

T26475481
Position Surface form Disambiguated ID Type / Status
Subject Leonardtown, Maryland E666019 entity
Predicate hasTransportation P105 FINISHED
Object Maryland Route 243
Maryland Route 243 is a short state highway in St. Mary's County that connects Leonardtown to nearby rural communities and local roads.
 E1873150 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 243 | Statement: [Leonardtown, Maryland, hasTransportation, Maryland Route 243]
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 243
Triple: [Leonardtown, Maryland, hasTransportation, Maryland Route 243]
Generated description
Maryland Route 243 is a short state highway in St. Mary's County that connects Leonardtown to nearby rural communities and local roads.

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_69ee883f80dc819090e311b022b78e02 completed April 26, 2026, 9:48 p.m.
NER Named-entity recognition batch_69f612cce1348190861a76259a2b9c85 completed May 2, 2026, 3:05 p.m.
NED1 Entity disambiguation (via context triple) batch_6a260bf2a7908190a85d232c9cd2281d completed June 8, 2026, 12:25 a.m.
NEDg Description generation batch_6a26184352288190aa777cc3a13d8412 completed June 8, 2026, 1:17 a.m.
NED2 Entity disambiguation (via description) batch_6a26189229c08190a9ad8cfafbae2251 completed June 8, 2026, 1:19 a.m.
Created at: April 27, 2026, 12:22 a.m.