Triple

T27385749
Position Surface form Disambiguated ID Type / Status
Subject Frank J. Goodnow E691367 entity
Predicate fullName P16 FINISHED
Object Frank Johnson Goodnow
Frank Johnson Goodnow was an influential American legal scholar and political scientist, often regarded as a founder of modern public administration in the United States.
 E1769411 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: Frank Johnson Goodnow | Statement: [Frank J. Goodnow, fullName, Frank Johnson Goodnow]
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: Frank Johnson Goodnow
Triple: [Frank J. Goodnow, fullName, Frank Johnson Goodnow]
Generated description
Frank Johnson Goodnow was an influential American legal scholar and political scientist, often regarded as a founder of modern public administration in the United States.

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_69ef520386788190bc92cfcd97ebb67a completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62c8af8508190bf5e40515d5d8f34 completed May 2, 2026, 4:55 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12a7eff6388190b3aff945062ea2fc completed May 24, 2026, 7:25 a.m.
NEDg Description generation batch_6a12a949b620819092007b2ee7e96064 completed May 24, 2026, 7:31 a.m.
NED2 Entity disambiguation (via description) batch_6a12aa9670988190be61c9aaa57b70c9 completed May 24, 2026, 7:36 a.m.
Created at: April 27, 2026, 12:24 p.m.