Triple

T38001103
Position Surface form Disambiguated ID Type / Status
Subject Moneta, Virginia E948099 entity
Predicate hasTransportation P105 FINISHED
Object U.S. Route 122
U.S. Route 122 is a former United States Numbered Highway that once served parts of Pennsylvania before being decommissioned and largely replaced by other routes.
 E2293270 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: U.S. Route 122 | Statement: [Moneta, Virginia, hasTransportation, U.S. Route 122]
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: U.S. Route 122
Triple: [Moneta, Virginia, hasTransportation, U.S. Route 122]
Generated description
U.S. Route 122 is a former United States Numbered Highway that once served parts of Pennsylvania before being decommissioned and largely replaced by other routes.

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_69f76efa37088190be5416b7ef1ca275 completed May 3, 2026, 3:51 p.m.
NER Named-entity recognition batch_69fbc91df97c8190ab8d16bfd5351228 completed May 6, 2026, 11:05 p.m.
NED1 Entity disambiguation (via context triple) batch_6a7a82074cf081908a11b29061b05d80 completed Aug. 11, 2026, 1:59 a.m.
NEDg Description generation batch_6a7a827ca7f48190a8030ef5fa00236c completed Aug. 11, 2026, 2:01 a.m.
NED2 Entity disambiguation (via description) batch_6a7a82c1908c8190993df247f038f2a3 completed Aug. 11, 2026, 2:02 a.m.
Created at: May 3, 2026, 4:20 p.m.