Triple

T30504876
Position Surface form Disambiguated ID Type / Status
Subject Rantoul Police Department E776239 entity
Predicate appliesLegalSystem P605 FINISHED
Object Rantoul municipal code
The Rantoul municipal code is the body of local laws and regulations governing the Village of Rantoul, Illinois, including public safety, policing, and community standards.
E1917748 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: Rantoul municipal code | Statement: [Rantoul Police Department, appliesLegalSystem, Rantoul municipal code]
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: Rantoul municipal code
Triple: [Rantoul Police Department, appliesLegalSystem, Rantoul municipal code]
Generated description
The Rantoul municipal code is the body of local laws and regulations governing the Village of Rantoul, Illinois, including public safety, policing, and community standards.

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_69f2249a155c8190b1d512106007e9bb completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f687b1f5e481908b418423c77edbec completed May 2, 2026, 11:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a27ac372384819088d2bb7272507d85 completed June 9, 2026, 6:01 a.m.
NEDg Description generation batch_6a27ad42b24481909895f7722fd747a8 completed June 9, 2026, 6:05 a.m.
NED2 Entity disambiguation (via description) batch_6a27ae21f75481909c18ec26978a3e79 completed June 9, 2026, 6:09 a.m.
Created at: April 29, 2026, 8:15 p.m.