Triple

T14720862
Position Surface form Disambiguated ID Type / Status
Subject Sykes E345808 entity
Predicate hasVariantSpelling P457 FINISHED
Object Sikes E605827 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: Sikes | Statement: [Sykes, hasVariantSpelling, Sikes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sikes
Context triple: [Sykes, hasVariantSpelling, Sikes]
  • A. Bill Sikes chosen
    Bill Sikes is a violent, menacing criminal and one of the primary antagonists in Charles Dickens's novel "Oliver Twist."
  • B. Fagin
    Fagin is a cunning, manipulative leader of a gang of child pickpockets in the musical "Oliver!", adapted from Charles Dickens' novel "Oliver Twist."
  • C. Ripper Roo
    Ripper Roo is a deranged, hyperactive kangaroo villain from the Crash Bandicoot video game series, known for his manic laughter and explosive attacks.
  • D. Bartholomew Green
    Bartholomew Green was a prominent early 18th-century Boston printer and publisher known for producing influential colonial American works.
  • E. Jonathan Wild
    Jonathan Wild was an infamous early 18th-century London crime boss and thief-taker who orchestrated and profited from organized crime while posing as a law enforcer.
  • 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_69d822e5911c8190ba589f957dbd9ba7 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69dec25d56fc8190871873ca55d49272 completed April 14, 2026, 10:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69fdf0957bb081908f1f382f3be8ec20 completed May 8, 2026, 2:17 p.m.
Created at: April 10, 2026, 1:29 a.m.