Triple

T29796187
Position Surface form Disambiguated ID Type / Status
Subject Connecticut Route 149 E756555 entity
Predicate hasJunctionWith P1018 FINISHED
Object Connecticut Route 151
Connecticut Route 151 is a state highway in Connecticut that serves local traffic by connecting several communities in the central part of the state.
 E1961819 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: Connecticut Route 151 | Statement: [Connecticut Route 149, hasJunctionWith, Connecticut Route 151]
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: Connecticut Route 151
Triple: [Connecticut Route 149, hasJunctionWith, Connecticut Route 151]
Generated description
Connecticut Route 151 is a state highway in Connecticut that serves local traffic by connecting several communities in the central part of the state.

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_69f22454583081908927516cb9938d1d completed April 29, 2026, 3:31 p.m.
NER Named-entity recognition batch_69f674e690f081908e0992c9ae27c71e completed May 2, 2026, 10:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2b074927008190ad5e7569f7b76e68 completed June 11, 2026, 7:06 p.m.
NEDg Description generation batch_6a2b07c942fc8190bcea459269394043 completed June 11, 2026, 7:08 p.m.
NED2 Entity disambiguation (via description) batch_6a2b0835e4e8819083ecc030b1ee6acd completed June 11, 2026, 7:10 p.m.
Created at: April 29, 2026, 5:15 p.m.