Triple

T17651266
Position Surface form Disambiguated ID Type / Status
Subject Queen Elizabeth II Highway E429494 entity
Predicate hasJunctionWith P1018 FINISHED
Object Highway 53
Highway 53 is a provincial roadway in Alberta, Canada, that serves as an east–west connector route intersecting major north–south highways.
 E2286639 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: Highway 53 | Statement: [Queen Elizabeth II Highway, hasJunctionWith, Highway 53]
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: Highway 53
Triple: [Queen Elizabeth II Highway, hasJunctionWith, Highway 53]
Generated description
Highway 53 is a provincial roadway in Alberta, Canada, that serves as an east–west connector route intersecting major north–south highways.

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_69d889e2c2608190b762e76d9b2262f1 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e46e3d4948819084de72bed922be6e completed April 19, 2026, 5:55 a.m.
NED1 Entity disambiguation (via context triple) batch_6a46cccfb8b4819081b34611991c298f completed July 2, 2026, 8:40 p.m.
NEDg Description generation batch_6a46cdb13a6c8190ba776993f909b28e completed July 2, 2026, 8:44 p.m.
NED2 Entity disambiguation (via description) batch_6a46cf484fc48190a1fdbbc8f14ad15f completed July 2, 2026, 8:51 p.m.
Created at: April 10, 2026, 6:05 a.m.