Triple

T24011938
Position Surface form Disambiguated ID Type / Status
Subject U.S. Highways in Nebraska E594553 entity
Predicate hasRoute P4374 FINISHED
Object U.S. Route 275 in Nebraska
U.S. Route 275 in Nebraska is a federal highway that runs through eastern Nebraska, connecting rural communities with larger cities and linking the state’s road network to neighboring regions.
E1621072 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: U.S. Route 275 in Nebraska | Statement: [U.S. Highways in Nebraska, hasRoute, U.S. Route 275 in Nebraska]
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: U.S. Route 275 in Nebraska
Triple: [U.S. Highways in Nebraska, hasRoute, U.S. Route 275 in Nebraska]
Generated description
U.S. Route 275 in Nebraska is a federal highway that runs through eastern Nebraska, connecting rural communities with larger cities and linking the state’s road network to neighboring regions.

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_69e288bc8f608190ac4af29f0bd1c744 completed April 17, 2026, 7:23 p.m.
NER Named-entity recognition batch_69f1d59d6a2c8190860a44c056030826 completed April 29, 2026, 9:55 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0fad01500c8190adcb71ffe8710e84 completed May 22, 2026, 1:10 a.m.
NEDg Description generation batch_6a0fae6318c8819099bf0565a01b5312 completed May 22, 2026, 1:16 a.m.
NED2 Entity disambiguation (via description) batch_6a0faf073c088190bbf21e4dd0434fc1 completed May 22, 2026, 1:19 a.m.
Created at: April 17, 2026, 9:41 p.m.