Triple

T31456046
Position Surface form Disambiguated ID Type / Status
Subject Connecticut expressways E802452 entity
Predicate hasComponent P35 FINISHED
Object Route 8 expressway
Route 8 expressway is a major north–south highway in Connecticut that connects the Bridgeport area on the coast to the Naugatuck Valley and cities such as Waterbury and Torrington.
E1967726 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 8 expressway | Statement: [Connecticut expressways, hasComponent, Route 8 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 8 expressway
Triple: [Connecticut expressways, hasComponent, Route 8 expressway]
Generated description
Route 8 expressway is a major north–south highway in Connecticut that connects the Bridgeport area on the coast to the Naugatuck Valley and cities such as Waterbury and Torrington.

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_6a2b2d700458819099b871c3b6c64014 completed June 11, 2026, 9:49 p.m.
NEDg Description generation batch_6a2b2f3ce7208190a8497ce44c6b24ad completed June 11, 2026, 9:57 p.m.
NED2 Entity disambiguation (via description) batch_6a2b2fd806b88190805273b42e62a52f completed June 11, 2026, 9:59 p.m.
Created at: April 30, 2026, 9:16 p.m.