Triple

T26215569
Position Surface form Disambiguated ID Type / Status
Subject Prince Township Council E655615 entity
Predicate jurisdiction P82 FINISHED
Object Prince Township, Ontario, Canada
Prince Township, Ontario, Canada is a small rural municipality in Northern Ontario located just west of the city of Sault Ste. Marie.
E1715613 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: Prince Township, Ontario, Canada | Statement: [Prince Township Council, jurisdiction, Prince Township, Ontario, Canada]
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: Prince Township, Ontario, Canada
Triple: [Prince Township Council, jurisdiction, Prince Township, Ontario, Canada]
Generated description
Prince Township, Ontario, Canada is a small rural municipality in Northern Ontario located just west of the city of Sault Ste. Marie.

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_69ee5b4a77e08190bfcb5f8ecdc55abd completed April 26, 2026, 6:36 p.m.
NER Named-entity recognition batch_69f60d1ade688190b3ffdae556e903a5 completed May 2, 2026, 2:41 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11858d438481908dd31a01c86974c8 completed May 23, 2026, 10:46 a.m.
NEDg Description generation batch_6a11861f5bd08190873109d86ffaca0a completed May 23, 2026, 10:49 a.m.
NED2 Entity disambiguation (via description) batch_6a1186f95c8c8190ab60afefe537a971 completed May 23, 2026, 10:52 a.m.
Created at: April 26, 2026, 8:54 p.m.