Triple

T31456058
Position Surface form Disambiguated ID Type / Status
Subject Connecticut expressways E802452 entity
Predicate hasComponent P35 FINISHED
Object Route 571 expressway
Route 571 expressway is a limited-access highway in Connecticut that serves as a key connector between local roads and major regional routes.
 E1979078 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: Route 571 expressway | Statement: [Connecticut expressways, hasComponent, Route 571 expressway]
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: Route 571 expressway
Triple: [Connecticut expressways, hasComponent, Route 571 expressway]
Generated description
Route 571 expressway is a limited-access highway in Connecticut that serves as a key connector between local roads and major regional 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_69f348c678ac81908a2e950867619061 completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6a14808548190afe3161c74e09c1b completed May 3, 2026, 1:13 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2e6580209c8190acfded23ba11b2c2 completed June 14, 2026, 8:25 a.m.
NEDg Description generation batch_6a2e663626b481909660c9d46e745387 completed June 14, 2026, 8:28 a.m.
NED2 Entity disambiguation (via description) batch_6a2e669724a8819090fc9237866ab385 completed June 14, 2026, 8:30 a.m.
Created at: April 30, 2026, 9:16 p.m.