Triple

T28311347
Position Surface form Disambiguated ID Type / Status
Subject Chicago North Side and North Shore region E714003 entity
Predicate includesNeighborhood P4813 FINISHED
Object Rogers Park
Rogers Park is a diverse, lakefront neighborhood on Chicago’s far North Side known for its cultural mix, historic architecture, and proximity to Loyola University.
E1825738 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: Rogers Park | Statement: [Chicago North Side and North Shore region, includesNeighborhood, Rogers Park]
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: Rogers Park
Triple: [Chicago North Side and North Shore region, includesNeighborhood, Rogers Park]
Generated description
Rogers Park is a diverse, lakefront neighborhood on Chicago’s far North Side known for its cultural mix, historic architecture, and proximity to Loyola University.

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_69efb5256afc8190b9322d25c3ae6320 completed April 27, 2026, 7:12 p.m.
NER Named-entity recognition batch_69f644e2e6608190957cfb801455930a completed May 2, 2026, 6:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1cb6c97e1c819094600a71ff2c9c61 completed May 31, 2026, 10:31 p.m.
NEDg Description generation batch_6a1cbadae2b88190923794f499874f0d completed May 31, 2026, 10:48 p.m.
NED2 Entity disambiguation (via description) batch_6a1cbb4e3c4081909221f3997a54efb7 completed May 31, 2026, 10:50 p.m.
Created at: April 27, 2026, 11:40 p.m.