Triple

T26930541
Position Surface form Disambiguated ID Type / Status
Subject Douglas community area E678201 entity
Predicate hasLandmark P105 FINISHED
Object Illinois College of Optometry
Illinois College of Optometry is a specialized professional school in Chicago dedicated to educating and training optometrists and advancing eye and vision care through clinical services and research.
E1748138 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: Illinois College of Optometry | Statement: [Douglas community area, hasLandmark, Illinois College of Optometry]
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: Illinois College of Optometry
Triple: [Douglas community area, hasLandmark, Illinois College of Optometry]
Generated description
Illinois College of Optometry is a specialized professional school in Chicago dedicated to educating and training optometrists and advancing eye and vision care through clinical services and research.

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_69eeeb4cac908190a45956c2993d1cc2 completed April 27, 2026, 4:51 a.m.
NER Named-entity recognition batch_69f620497848819087881b4f82c7bc22 completed May 2, 2026, 4:03 p.m.
NED1 Entity disambiguation (via context triple) batch_6a121ebf01f88190ba2788465bd2c497 completed May 23, 2026, 9:40 p.m.
NEDg Description generation batch_6a121f7b308c8190a2667f99b45cf2ab completed May 23, 2026, 9:43 p.m.
NED2 Entity disambiguation (via description) batch_6a1220284ddc819085b3ca2cad3fbfa9 completed May 23, 2026, 9:46 p.m.
Created at: April 27, 2026, 6:12 a.m.