Triple

T27002224
Position Surface form Disambiguated ID Type / Status
Subject Washburn University E680140 entity
Predicate hasAlumni P51 FINISHED
Object Kay McFarland
Kay McFarland was the first woman to serve as a justice and later chief justice of the Kansas Supreme Court, becoming a trailblazer for women in the American judiciary.
 E1816062 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: Kay McFarland | Statement: [Washburn University, hasAlumni, Kay McFarland]
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: Kay McFarland
Triple: [Washburn University, hasAlumni, Kay McFarland]
Generated description
Kay McFarland was the first woman to serve as a justice and later chief justice of the Kansas Supreme Court, becoming a trailblazer for women in the American judiciary.

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_69eeeb52908c8190bd246244686aa455 completed April 27, 2026, 4:51 a.m.
NER Named-entity recognition batch_69f621ceaaf481908b1b31f01591a427 completed May 2, 2026, 4:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1632d673fc8190ba6ae476a0250582 completed May 26, 2026, 11:55 p.m.
NEDg Description generation batch_6a163460070c8190adfe7eca682d7521 completed May 27, 2026, 12:01 a.m.
NED2 Entity disambiguation (via description) batch_6a16356507b881908d99d50c159f8118 completed May 27, 2026, 12:05 a.m.
Created at: April 27, 2026, 6:58 a.m.