Triple

T35064007
Position Surface form Disambiguated ID Type / Status
Subject G. G. Brown Laboratory E1011676 entity
Predicate namedAfter P63 FINISHED
Object George Granger Brown
George Granger Brown was an American chemical engineer and influential educator whose work significantly advanced chemical engineering research and education.
 E2123759 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: George Granger Brown | Statement: [G. G. Brown Laboratory, namedAfter, George Granger Brown]
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: George Granger Brown
Triple: [G. G. Brown Laboratory, namedAfter, George Granger Brown]
Generated description
George Granger Brown was an American chemical engineer and influential educator whose work significantly advanced chemical engineering research and education.

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_69f76dd09c308190a523454853ce842b completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f78611cef081908cf99c196df5b068 completed May 3, 2026, 5:29 p.m.
NED1 Entity disambiguation (via context triple) batch_6a37c64036608190bc1311956b4f1572 completed June 21, 2026, 11:08 a.m.
NEDg Description generation batch_6a37c6cd2c6c81908b0259556d3da946 completed June 21, 2026, 11:11 a.m.
NED2 Entity disambiguation (via description) batch_6a37c75384a081909fe2398e87ba9186 completed June 21, 2026, 11:13 a.m.
Created at: May 3, 2026, 4:01 p.m.