Triple

T5947804
Position Surface form Disambiguated ID Type / Status
Subject Ronald George Wreyford Norrish E132321 entity
Predicate birthPlace P1 FINISHED
Object Cambridge E492 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: Cambridge | Statement: [Ronald George Wreyford Norrish, birthPlace, Cambridge]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cambridge
Context triple: [Ronald George Wreyford Norrish, birthPlace, Cambridge]
  • A. Cambridge
    Cambridge is a town in New Zealand known for its picturesque rural setting, equestrian culture, and proximity to the Waikato River.
  • B. Cambridge
    Cambridge is a historic and academically renowned city in Massachusetts, best known as the home of Harvard University and the Massachusetts Institute of Technology (MIT).
  • C. Cambridge
    Cambridge is a prominent city in the Greater Boston area best known as the home of Harvard University and the Massachusetts Institute of Technology (MIT).
  • D. Cambridge
    Cambridge is a city in southwestern Ontario, Canada, known as part of the Regional Municipality of Waterloo and situated along the Grand River.
  • E. Cambridge, England chosen
    Cambridge, England is a historic university city on the River Cam renowned for the University of Cambridge and its longstanding contributions to education, science, and culture.
  • 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_69c00869d3308190af89b2453e0f7546 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c0397c80708190a4778fdb353314b7 completed March 22, 2026, 6:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69c108269e6081909fcb6b880fc92011 completed March 23, 2026, 9:30 a.m.
Created at: March 22, 2026, 4:01 p.m.