Triple

T799273
Position Surface form Disambiguated ID Type / Status
Subject Ann Sadler E17091 entity
Predicate spouse P13 FINISHED
Object John Harvard E103 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: John Harvard | Statement: [Ann Sadler, spouse, John Harvard]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: John Harvard
Context triple: [Ann Sadler, spouse, John Harvard]
  • A. John Harvard chosen
    John Harvard was a 17th-century English clergyman and benefactor whose substantial bequest helped establish the institution that became Harvard University.
  • B. Thomas Harvard
    Thomas Harvard was a 17th-century Englishman known primarily as the brother of John Harvard, the clergyman and benefactor after whom Harvard University is named.
  • C. Charles Eliot
    Charles Eliot was a prominent American landscape architect of the late 19th century, known for his influential work in urban park and parkway design around Boston.
  • D. Robert Harvard
    Robert Harvard was the father of John Harvard, the English clergyman whose bequest helped found Harvard College in colonial America.
  • E. Charles W. Eliot
    Charles W. Eliot was a prominent American academic and long-serving president of Harvard University who played a major role in modernizing higher education in the United States.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69a49378b9c48190adbf5f62e5b7aca1 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a7cb26dc8190bdd3a278b8695873 completed March 1, 2026, 8:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69a76d818e208190a8f3b165c0770e09 completed March 3, 2026, 11:23 p.m.
Created at: March 1, 2026, 7:38 p.m.