Triple

T38603371
Position Surface form Disambiguated ID Type / Status
Subject I-264 (Watterson Expressway) E934277 entity
Predicate hasJunctionWith P1018 FINISHED
Object US 31W
US 31W is a U.S. highway running through Kentucky and Tennessee, serving as a major north–south route that parallels Interstate 65 and connects cities such as Louisville, Bowling Green, and Nashville.
E2277136 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: US 31W | Statement: [I-264 (Watterson Expressway), hasJunctionWith, US 31W]
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: US 31W
Triple: [I-264 (Watterson Expressway), hasJunctionWith, US 31W]
Generated description
US 31W is a U.S. highway running through Kentucky and Tennessee, serving as a major north–south route that parallels Interstate 65 and connects cities such as Louisville, Bowling Green, and Nashville.

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_69f76ecc17688190b389b693a5927501 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fcd95789ac8190898847981a92b0af completed May 7, 2026, 6:26 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41eaa85d308190b74e95c3f174c698 completed June 29, 2026, 3:46 a.m.
NEDg Description generation batch_6a41ec152f388190923fbcfe62388d0c completed June 29, 2026, 3:52 a.m.
NED2 Entity disambiguation (via description) batch_6a41ed488640819081569004fb07da03 completed June 29, 2026, 3:58 a.m.
Created at: May 3, 2026, 4:32 p.m.