Triple

T30397281
Position Surface form Disambiguated ID Type / Status
Subject Victor and Elizabeth Atkins Professor of Chemistry E773252 entity
Predicate namedAfter P63 FINISHED
Object Elizabeth Atkins
Elizabeth Atkins is the namesake of the Victor and Elizabeth Atkins Professorship of Chemistry, indicating her significant contributions or philanthropic support to the field.
E2007689 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: Elizabeth Atkins | Statement: [Victor and Elizabeth Atkins Professor of Chemistry, namedAfter, Elizabeth Atkins]
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: Elizabeth Atkins
Triple: [Victor and Elizabeth Atkins Professor of Chemistry, namedAfter, Elizabeth Atkins]
Generated description
Elizabeth Atkins is the namesake of the Victor and Elizabeth Atkins Professorship of Chemistry, indicating her significant contributions or philanthropic support to the field.

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_69f2248facd48190b183c3f3ca6daef7 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f685aef340819081190a2397b94251 completed May 2, 2026, 11:15 p.m.
NED1 Entity disambiguation (via context triple) batch_6a34665207a081908152ee52b113938a completed June 18, 2026, 9:42 p.m.
NEDg Description generation batch_6a346777b13481908d5d05cb281e940d completed June 18, 2026, 9:47 p.m.
NED2 Entity disambiguation (via description) batch_6a3468279dbc8190b5efcecd6f4aa23c completed June 18, 2026, 9:50 p.m.
Created at: April 29, 2026, 8:03 p.m.