Triple

T21655583
Position Surface form Disambiguated ID Type / Status
Subject Connecticut Route 8 E534454 entity
Predicate hasJunctionWith P1018 FINISHED
Object Connecticut Route 73
Connecticut Route 73 is a short state highway in western Connecticut that connects the city of Waterbury with the town of Watertown, serving as a local connector to larger regional routes.
 E1707750 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 73 | Statement: [Connecticut Route 8, hasJunctionWith, Connecticut Route 73]
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 73
Triple: [Connecticut Route 8, hasJunctionWith, Connecticut Route 73]
Generated description
Connecticut Route 73 is a short state highway in western Connecticut that connects the city of Waterbury with the town of Watertown, serving as a local connector to larger 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_69e0c467e1f48190af2650b19175abc4 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69ef59185b208190a2cf4b8f54a2c231 completed April 27, 2026, 12:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a111ad3ccd88190b64f18797bded703 completed May 23, 2026, 3:11 a.m.
NEDg Description generation batch_6a111d4d7ba88190ad3174850da37549 completed May 23, 2026, 3:21 a.m.
NED2 Entity disambiguation (via description) batch_6a111dab6a38819095dcc72b1c1b928b completed May 23, 2026, 3:23 a.m.
Created at: April 16, 2026, 6:36 p.m.