Triple

T11402700
Position Surface form Disambiguated ID Type / Status
Subject Reading, Ohio E270154 entity
Predicate hasMajorRoad P385 FINISHED
Object U.S. Route 42
U.S. Route 42 is a U.S. highway running generally northeast–southwest through several Midwestern states, including Ohio, connecting cities such as Louisville, Lexington, Cincinnati, and Cleveland.
 E1626822 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 42 | Statement: [Reading, Ohio, hasMajorRoad, U.S. Route 42]
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 42
Triple: [Reading, Ohio, hasMajorRoad, U.S. Route 42]
Generated description
U.S. Route 42 is a U.S. highway running generally northeast–southwest through several Midwestern states, including Ohio, connecting cities such as Louisville, Lexington, Cincinnati, and Cleveland.

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_69d6aacdbc6c8190af6dc3d5f5d22836 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d8014ab46881909fa1d425926c617b completed April 9, 2026, 7:43 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0fc98202248190a2e3ee03c4e7078a completed May 22, 2026, 3:12 a.m.
NEDg Description generation batch_6a0fcade9db88190b79f8f03c9b5f51f completed May 22, 2026, 3:17 a.m.
NED2 Entity disambiguation (via description) batch_6a0fcb724a888190838a30e05e556421 completed May 22, 2026, 3:20 a.m.
Created at: April 8, 2026, 9:34 p.m.