Triple

T36393395
Position Surface form Disambiguated ID Type / Status
Subject Robertson County, Kentucky E896393 entity
Predicate namedAfter P63 FINISHED
Object George Robertson
George Robertson was an American jurist and politician from Kentucky, notably serving as a U.S. Congressman and chief justice of the Kentucky Court of Appeals in the early 19th century.
E2188412 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 Robertson | Statement: [Robertson County, Kentucky, namedAfter, George Robertson]
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 Robertson
Triple: [Robertson County, Kentucky, namedAfter, George Robertson]
Generated description
George Robertson was an American jurist and politician from Kentucky, notably serving as a U.S. Congressman and chief justice of the Kentucky Court of Appeals in the early 19th century.

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_69f76e52e3108190becf70b090ae7bd6 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7bcda95e48190a7fb9e56b58233de completed May 3, 2026, 9:23 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39e6c908b08190ac34fafe8663ffe2 completed June 23, 2026, 1:52 a.m.
NEDg Description generation batch_6a39e82896d08190851ad8bb6a793b6a completed June 23, 2026, 1:58 a.m.
NED2 Entity disambiguation (via description) batch_6a39e88d8954819083d2669a9223a0aa completed June 23, 2026, 1:59 a.m.
Created at: May 3, 2026, 4:10 p.m.