Triple

T22541617
Position Surface form Disambiguated ID Type / Status
Subject d’Urban E557303 entity
Predicate usedBy P260 FINISHED
Object Benjamin d’Urban NE NERFINISHED

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: Benjamin d’Urban | Statement: [d’Urban, usedBy, Benjamin d’Urban]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Benjamin d’Urban
Context triple: [d’Urban, usedBy, Benjamin d’Urban]
  • A. Benjamin d’Urban chosen
    Benjamin d’Urban was a 19th-century British army officer and colonial administrator whose name was given to the South African city of Durban.
  • B. Louis Scatcherd
    Louis Scatcherd is a character in Anthony Trollope’s novel "Doctor Thorne," known as the dissipated son of a wealthy railway magnate whose lifestyle and inheritance are central to the story’s social and moral conflicts.
  • C. Julian Seward
    Julian Seward is a British computer programmer best known for creating the bzip2 compression tool and the Valgrind programming debugger.
  • D. Lionel Belmore
    Lionel Belmore was an English character actor and film director known for his prolific work in early 20th-century stage and cinema, including numerous Hollywood productions.
  • E. Michael Balfour
    Michael Balfour was a British character actor known for his numerous supporting roles in film and television from the 1940s through the 1980s.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e58662081909ae346ab384514ca completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15f3251808190a72b849157854d8d completed April 29, 2026, 1:30 a.m.
Created at: April 16, 2026, 8:51 p.m.